From ba97eba164195c6262aa5fefa1f5751587cb4e9d Mon Sep 17 00:00:00 2001
From: Joseph Larmarange <joseph@larmarange.net>
Date: Fri, 27 Sep 2024 02:53:07 +0200
Subject: [PATCH] rebuild

---
 docs/analyses/contrastes.html                 |    4 +-
 docs/analyses/estimations-marginales.html     |    8 +-
 docs/analyses/ggplot2.html                    |    4 +-
 docs/analyses/interactions.html               |    4 +-
 docs/analyses/likert.html                     |    4 +-
 docs/analyses/multicolinearite.html           |    4 +-
 docs/analyses/regression-lineaire.html        |   12 +-
 .../regression-logistique-binaire.html        |   20 +-
 docs/analyses/selection-modele-pas-a-pas.html |  180 +--
 .../fig-comparaison-modeles-dodge-1.pdf       |  Bin 8506 -> 0 bytes
 .../fig-comparaison-modeles-faceted-1.pdf     |  Bin 8677 -> 0 bytes
 docs/analyses/statistique-bivariee.html       |    4 +-
 docs/analyses/statistique-univariee.html      |   18 +-
 .../analyse-factorielle.html                  |   26 +-
 docs/analyses_avancees/analyse-survie.html    | 1012 +++++++++--------
 .../figure-html/unnamed-chunk-27-1.png        |  Bin 0 -> 12020 bytes
 .../figure-html/unnamed-chunk-28-1.png        |  Bin 0 -> 13880 bytes
 docs/analyses_avancees/choix-modele.html      |    2 +-
 ...lassification-ascendante-hierarchique.html |    4 +-
 docs/analyses_avancees/modeles-comptage.html  |    4 +-
 docs/analyses_avancees/modeles-incidence.html |   10 +-
 .../modeles-zero-inflated.html                |   12 +-
 .../regression-logistique-multinomiale.html   |    4 +-
 .../regression-logistique-ordinale.html       |   10 +-
 docs/bases/attributs.html                     |    2 +-
 docs/bases/listes.html                        |    2 +-
 docs/bases/packages.html                      |    6 +-
 docs/bases/tableaux_donnees.html              |    6 +-
 docs/bases/tibbles.html                       |    2 +-
 docs/bases/vecteurs.html                      |    2 +-
 .../donnees_ponderees/analyses-bivariees.html |    2 +-
 .../graphiques-ponderes.html                  |    4 +-
 docs/donnees_ponderees/manipulation.html      |    2 +-
 .../plan-echantillonnage.html                 |    2 +-
 ...egression-logistique-binaire-ponderee.html |    4 +-
 docs/extra/ressources-documentaires.html      |    2 +-
 docs/index.html                               |    6 +-
 docs/manipulation/combiner-variables.html     |    2 +-
 docs/manipulation/couleurs.html               |    4 +-
 docs/manipulation/dplyr.html                  |    2 +-
 docs/manipulation/etiquettes-valeurs.html     |    6 +-
 docs/manipulation/etiquettes-variables.html   |    4 +-
 docs/manipulation/facteurs.html               |    4 +-
 docs/manipulation/formater-nombre.html        |    2 +-
 docs/manipulation/import-export.html          |    4 +-
 docs/manipulation/pipe.html                   |    4 +-
 docs/manipulation/valeurs-manquantes.html     |    2 +-
 .../conditions-logiques.html                  |    6 +-
 docs/manipulation_avancee/dates.html          |    2 +-
 .../fonctions-a-fenetre.html                  |    2 +-
 docs/manipulation_avancee/fusion-tables.html  |    2 +-
 docs/manipulation_avancee/stringr.html        |    2 +-
 docs/manipulation_avancee/tidyr.html          |    4 +-
 .../transformations-multiples.html            |    2 +-
 docs/search.json                              |   36 +-
 55 files changed, 806 insertions(+), 672 deletions(-)
 delete mode 100644 docs/analyses/selection-modele-pas-a-pas_files/figure-pdf/fig-comparaison-modeles-dodge-1.pdf
 delete mode 100644 docs/analyses/selection-modele-pas-a-pas_files/figure-pdf/fig-comparaison-modeles-faceted-1.pdf
 create mode 100644 docs/analyses_avancees/analyse-survie_files/figure-html/unnamed-chunk-27-1.png
 create mode 100644 docs/analyses_avancees/analyse-survie_files/figure-html/unnamed-chunk-28-1.png

diff --git a/docs/analyses/contrastes.html b/docs/analyses/contrastes.html
index 728ffa3..500f8e9 100644
--- a/docs/analyses/contrastes.html
+++ b/docs/analyses/contrastes.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -6549,7 +6549,7 @@ <h1 class="title"><span id="sec-contrastes" class="quarto-section-identifier"><s
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"loop":false,"openEffect":"zoom","descPosition":"bottom","selector":".lightbox","closeEffect":"zoom"});
+</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","openEffect":"zoom","loop":false,"descPosition":"bottom","selector":".lightbox"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/estimations-marginales.html b/docs/analyses/estimations-marginales.html
index 6bdd44d..eb068ea 100644
--- a/docs/analyses/estimations-marginales.html
+++ b/docs/analyses/estimations-marginales.html
@@ -358,7 +358,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -8461,8 +8461,8 @@ <h1 class="title"><span id="sec-estimations-marginales" class="quarto-section-id
 });
 </script><script src="https://giscus.app/client.js" data-repo="larmarange/guide-R" data-repo-id="R_kgDOH6-xnw" data-category="Forum" data-category-id="DIC_kwDOH6-xn84CRLQy" data-mapping="pathname" data-reactions-enabled="1" data-emit-metadata="0" data-input-position="top" data-theme="light" data-lang="fr" crossorigin="anonymous" data-loading="lazy" async="">
 </script><input type="hidden" id="giscus-base-theme" value="light"><input type="hidden" id="giscus-alt-theme" value="dark"><nav class="page-navigation"><div class="nav-page nav-page-previous">
-      <a href="../analyses/selection-modele-pas-a-pas.html" class="pagination-link" aria-label="Sélection pas à pas d'un modèle">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span>
+      <a href="../analyses/selection-modele-pas-a-pas.html" class="pagination-link" aria-label="Sélection pas à pas d'un modèle réduit">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
@@ -8516,7 +8516,7 @@ <h1 class="title"><span id="sec-estimations-marginales" class="quarto-section-id
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","selector":".lightbox","loop":false,"descPosition":"bottom","openEffect":"zoom"});
+</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","closeEffect":"zoom","openEffect":"zoom","loop":false,"descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/ggplot2.html b/docs/analyses/ggplot2.html
index 0ebced8..fd92b8f 100644
--- a/docs/analyses/ggplot2.html
+++ b/docs/analyses/ggplot2.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -1462,7 +1462,7 @@ <h1 class="title"><span id="sec-ggplot2" class="quarto-section-identifier"><span
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"descPosition":"bottom","openEffect":"zoom","closeEffect":"zoom","selector":".lightbox","loop":false});
+</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","selector":".lightbox","loop":false,"openEffect":"zoom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/interactions.html b/docs/analyses/interactions.html
index cf3dee4..9a70bb1 100644
--- a/docs/analyses/interactions.html
+++ b/docs/analyses/interactions.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -2839,7 +2839,7 @@ <h1 class="title"><span id="sec-interactions" class="quarto-section-identifier">
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","selector":".lightbox","openEffect":"zoom","loop":false,"descPosition":"bottom"});
+</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","closeEffect":"zoom","loop":false,"openEffect":"zoom","descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/likert.html b/docs/analyses/likert.html
index 3dd3d9f..03bff50 100644
--- a/docs/analyses/likert.html
+++ b/docs/analyses/likert.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -3590,7 +3590,7 @@ <h1 class="title"><span id="sec-likert" class="quarto-section-identifier"><span
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"loop":false,"descPosition":"bottom","openEffect":"zoom","closeEffect":"zoom","selector":".lightbox"});
+</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","loop":false,"openEffect":"zoom","selector":".lightbox","descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/docs/analyses/multicolinearite.html b/docs/analyses/multicolinearite.html
index e3993f8..4c69b88 100644
--- a/docs/analyses/multicolinearite.html
+++ b/docs/analyses/multicolinearite.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -2085,7 +2085,7 @@ <h1 class="title"><span id="sec-multicolinearite" class="quarto-section-identifi
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","descPosition":"bottom","openEffect":"zoom","loop":false,"closeEffect":"zoom"});
+</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","closeEffect":"zoom","descPosition":"bottom","openEffect":"zoom","loop":false});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/regression-lineaire.html b/docs/analyses/regression-lineaire.html
index aa44499..349028f 100644
--- a/docs/analyses/regression-lineaire.html
+++ b/docs/analyses/regression-lineaire.html
@@ -358,7 +358,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -607,7 +607,7 @@ <h1 class="title"><span id="sec-regression-lineaire" class="quarto-section-ident
 <div class="cell-output-display page-columns page-full">
 <div id="fig-rel-petal.width-petal.length" class="quarto-figure quarto-figure-center quarto-float anchored page-columns page-full">
 <figure class="quarto-float quarto-float-fig figure page-columns page-full"><div aria-describedby="fig-rel-petal.width-petal.length-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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+<a href="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-1"><img src="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-rel-petal.width-petal.length-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;21.1: Relation entre la largeur et la longueur des pétales (nuage de points)
@@ -626,7 +626,7 @@ <h1 class="title"><span id="sec-regression-lineaire" class="quarto-section-ident
 <div class="cell-output-display page-columns page-full">
 <div id="fig-rel-petal.width-petal.length-lm" class="quarto-figure quarto-figure-center quarto-float anchored page-columns page-full">
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-<a href="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm-1.png" class="lightbox" data-glightbox="description: .lightbox-desc-2" data-gallery="quarto-lightbox-gallery-2"><img src="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm-1.png" class="img-fluid figure-img" width="672"></a>
+<a href="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-2" data-glightbox="description: .lightbox-desc-2"><img src="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-rel-petal.width-petal.length-lm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;21.2: Relation linéaire entre la largeur et la longueur des pétales
@@ -667,7 +667,7 @@ <h1 class="title"><span id="sec-regression-lineaire" class="quarto-section-ident
 <div class="cell-output-display page-columns page-full">
 <div id="fig-rel-petal.width-petal.length-lm2" class="quarto-figure quarto-figure-center quarto-float anchored page-columns page-full">
 <figure class="quarto-float quarto-float-fig figure page-columns page-full"><div aria-describedby="fig-rel-petal.width-petal.length-lm2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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+<a href="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm2-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-3" data-glightbox="description: .lightbox-desc-3"><img src="regression-lineaire_files/figure-html/fig-rel-petal.width-petal.length-lm2-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-rel-petal.width-petal.length-lm2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;21.3: Relation linéaire entre la largeur et la longueur des pétales (représentation graphique de l’intercept)
@@ -2399,7 +2399,7 @@ <h1 class="title"><span id="sec-regression-lineaire" class="quarto-section-ident
 <div class="cell-output-display page-columns page-full">
 <div id="fig-forest-plot-lm" class="quarto-figure quarto-figure-center quarto-float anchored page-columns page-full">
 <figure class="quarto-float quarto-float-fig figure page-columns page-full"><div aria-describedby="fig-forest-plot-lm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<a href="regression-lineaire_files/figure-html/fig-forest-plot-lm-1.png" class="lightbox" data-glightbox="description: .lightbox-desc-4" data-gallery="quarto-lightbox-gallery-4"><img src="regression-lineaire_files/figure-html/fig-forest-plot-lm-1.png" class="img-fluid figure-img" width="672"></a>
+<a href="regression-lineaire_files/figure-html/fig-forest-plot-lm-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-4" data-glightbox="description: .lightbox-desc-4"><img src="regression-lineaire_files/figure-html/fig-forest-plot-lm-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-forest-plot-lm-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;21.4: un graphique en forêt des coefficients du modèle
@@ -3024,7 +3024,7 @@ <h1 class="title"><span id="sec-regression-lineaire" class="quarto-section-ident
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"openEffect":"zoom","selector":".lightbox","closeEffect":"zoom","loop":false,"descPosition":"bottom"});
+</footer><script>var lightboxQuarto = GLightbox({"descPosition":"bottom","closeEffect":"zoom","selector":".lightbox","loop":false,"openEffect":"zoom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/docs/analyses/regression-logistique-binaire.html b/docs/analyses/regression-logistique-binaire.html
index 2c60c2b..392606e 100644
--- a/docs/analyses/regression-logistique-binaire.html
+++ b/docs/analyses/regression-logistique-binaire.html
@@ -358,7 +358,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -2215,7 +2215,7 @@ <h1 class="title"><span id="sec-regression-logistique-binaire" class="quarto-sec
 <span id="cb32-11"><a href="#cb32-11"></a>  )</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display page-columns page-full">
 <div class="quarto-figure quarto-figure-center page-columns page-full">
-<figure class="figure page-columns page-full"><p><a href="regression-logistique-binaire_files/figure-html/breakdown-individu3-1.png" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1" title="Décomposition de la probabilité de faire du sport de l’individu 3"><img src="regression-logistique-binaire_files/figure-html/breakdown-individu3-1.png" class="img-fluid figure-img" width="672" alt="Décomposition de la probabilité de faire du sport de l’individu 3"></a></p>
+<figure class="figure page-columns page-full"><p><a href="regression-logistique-binaire_files/figure-html/breakdown-individu3-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-1" title="Décomposition de la probabilité de faire du sport de l’individu 3"><img src="regression-logistique-binaire_files/figure-html/breakdown-individu3-1.png" class="img-fluid figure-img" width="672" alt="Décomposition de la probabilité de faire du sport de l’individu 3"></a></p>
 <figcaption class="margin-caption">Décomposition de la probabilité de faire du sport de l’individu 3</figcaption></figure>
 </div>
 </div>
@@ -2886,7 +2886,7 @@ <h1 class="title"><span id="sec-regression-logistique-binaire" class="quarto-sec
 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;22.1: Facteurs associés à la pratique d’un sport (<em>forest plot</em>)
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 Figure&nbsp;22.2: Facteurs associés à la pratique d’un sport (<em>forest plot</em> avec table des coefficients)
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 Figure&nbsp;22.3: Facteurs associés à la pratique d’un sport avec modelplot()
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;22.4: Odds Ratios associés à la pratique d’un sport avec modelplot()
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 Figure&nbsp;22.5: Facteurs associés à la pratique d’un sport (<em>pairwise contrasts</em>)
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+      <a href="../analyses/selection-modele-pas-a-pas.html" class="pagination-link" aria-label="Sélection pas à pas d'un modèle réduit">
+        <span class="nav-page-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span> <i class="bi bi-arrow-right-short"></i>
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diff --git a/docs/analyses/selection-modele-pas-a-pas.html b/docs/analyses/selection-modele-pas-a-pas.html
index 0530729..622a1e2 100644
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+<title>guide-R - 23&nbsp; Sélection pas à pas d’un modèle réduit</title>
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+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../analyses/ggplot2.html"><strong>Analyses</strong></a></li><li class="breadcrumb-item"><a href="../analyses/selection-modele-pas-a-pas.html"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></a></li></ol></nav>
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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 Figure&nbsp;23.1: Comparaison visuelle des deux modèles (<em>dodge</em>)
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 Figure&nbsp;23.2: Comparaison visuelle des deux modèles (<em>faceted</em>)
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-#tgaihvvjnx .gt_stub {
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 }
 
-#tgaihvvjnx .gt_stub_row_group {
+#jddlkducpu .gt_stub_row_group {
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-#tgaihvvjnx .gt_row_group_first td {
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-#tgaihvvjnx .gt_summary_row {
+#jddlkducpu .gt_summary_row {
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@@ -1367,16 +1367,16 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   padding-right: 5px;
 }
 
-#tgaihvvjnx .gt_first_summary_row {
+#jddlkducpu .gt_first_summary_row {
   border-top-style: solid;
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 }
 
-#tgaihvvjnx .gt_first_summary_row.thick {
+#jddlkducpu .gt_first_summary_row.thick {
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+#jddlkducpu .gt_last_summary_row {
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@@ -1386,7 +1386,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   border-bottom-color: #D3D3D3;
 }
 
-#tgaihvvjnx .gt_grand_summary_row {
+#jddlkducpu .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1396,7 +1396,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   padding-right: 5px;
 }
 
-#tgaihvvjnx .gt_first_grand_summary_row {
+#jddlkducpu .gt_first_grand_summary_row {
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   padding-bottom: 8px;
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@@ -1406,7 +1406,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   border-top-color: #D3D3D3;
 }
 
-#tgaihvvjnx .gt_last_grand_summary_row_top {
+#jddlkducpu .gt_last_grand_summary_row_top {
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   padding-bottom: 8px;
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@@ -1416,11 +1416,11 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   border-bottom-color: #D3D3D3;
 }
 
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+#jddlkducpu .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
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+#jddlkducpu .gt_table_body {
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@@ -1429,7 +1429,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   border-bottom-color: #D3D3D3;
 }
 
-#tgaihvvjnx .gt_footnotes {
+#jddlkducpu .gt_footnotes {
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@@ -1443,7 +1443,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
   border-right-color: #D3D3D3;
 }
 
-#tgaihvvjnx .gt_footnote {
+#jddlkducpu .gt_footnote {
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   padding-right: 5px;
 }
 
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+#jddlkducpu .gt_sourcenotes {
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@@ -1466,7 +1466,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
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+#jddlkducpu .gt_sourcenote {
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   padding-top: 4px;
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   padding-right: 5px;
 }
 
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+#jddlkducpu .gt_left {
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+#jddlkducpu .gt_indent_5 {
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-#tgaihvvjnx .katex-display {
+#jddlkducpu .katex-display {
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-#tgaihvvjnx div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
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@@ -1791,25 +1791,41 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
 <div class="cell">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1"></a>step_with_na <span class="ot">&lt;-</span> <span class="cf">function</span>(model,</span>
 <span id="cb45-2"><a href="#cb45-2"></a>                         <span class="at">full_data =</span> <span class="cn">NULL</span>, ...) {</span>
-<span id="cb45-3"><a href="#cb45-3"></a>  <span class="co"># assign the model in parent environment</span></span>
-<span id="cb45-4"><a href="#cb45-4"></a>  <span class="fu">assign</span>(<span class="st">"...full.model"</span>, model, <span class="at">pos =</span> <span class="dv">1</span>) </span>
-<span id="cb45-5"><a href="#cb45-5"></a>  <span class="co"># refit the model without NAs</span></span>
-<span id="cb45-6"><a href="#cb45-6"></a>  model_no_na <span class="ot">&lt;-</span> <span class="fu">update</span>(</span>
-<span id="cb45-7"><a href="#cb45-7"></a>    ...full.model,</span>
-<span id="cb45-8"><a href="#cb45-8"></a>    <span class="at">formula =</span> <span class="fu">terms</span>(...full.model),</span>
-<span id="cb45-9"><a href="#cb45-9"></a>    <span class="at">data =</span> <span class="fu">model.frame</span>(...full.model))</span>
-<span id="cb45-10"><a href="#cb45-10"></a>  <span class="co"># apply step()</span></span>
-<span id="cb45-11"><a href="#cb45-11"></a>  model_simplified <span class="ot">&lt;-</span> <span class="fu">step</span>(model_no_na, ...)</span>
-<span id="cb45-12"><a href="#cb45-12"></a>  <span class="co"># recompute simplified model using full data</span></span>
-<span id="cb45-13"><a href="#cb45-13"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(full_data)) {</span>
-<span id="cb45-14"><a href="#cb45-14"></a>    <span class="fu">update</span>(model,</span>
-<span id="cb45-15"><a href="#cb45-15"></a>           <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified))</span>
-<span id="cb45-16"><a href="#cb45-16"></a>  } <span class="cf">else</span> {</span>
-<span id="cb45-17"><a href="#cb45-17"></a>    <span class="fu">update</span>(model,</span>
-<span id="cb45-18"><a href="#cb45-18"></a>           <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified),</span>
-<span id="cb45-19"><a href="#cb45-19"></a>           <span class="at">data =</span> full_data)</span>
-<span id="cb45-20"><a href="#cb45-20"></a>  }</span>
-<span id="cb45-21"><a href="#cb45-21"></a>}</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb45-3"><a href="#cb45-3"></a>  <span class="co"># data with no NAs</span></span>
+<span id="cb45-4"><a href="#cb45-4"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(full_data)) {</span>
+<span id="cb45-5"><a href="#cb45-5"></a>    ...data_no_na <span class="ot">&lt;-</span> <span class="fu">model.frame</span>(model)</span>
+<span id="cb45-6"><a href="#cb45-6"></a>  } <span class="cf">else</span> {</span>
+<span id="cb45-7"><a href="#cb45-7"></a>    ...data_no_na <span class="ot">&lt;-</span> <span class="fu">get_all_vars</span>(model, <span class="at">data =</span> full_data)</span>
+<span id="cb45-8"><a href="#cb45-8"></a>    ...data_no_na <span class="ot">&lt;-</span> ...data_no_na[<span class="fu">complete.cases</span>(...data_no_na), ]</span>
+<span id="cb45-9"><a href="#cb45-9"></a>  }</span>
+<span id="cb45-10"><a href="#cb45-10"></a>  </span>
+<span id="cb45-11"><a href="#cb45-11"></a>  <span class="co"># assign ...data_no_na in parent.frame()</span></span>
+<span id="cb45-12"><a href="#cb45-12"></a>  <span class="fu">assign</span>(<span class="st">"...data_no_na"</span>, ...data_no_na, <span class="at">envir =</span> <span class="fu">parent.frame</span>())</span>
+<span id="cb45-13"><a href="#cb45-13"></a>  </span>
+<span id="cb45-14"><a href="#cb45-14"></a>  <span class="co"># refit the model without NAs</span></span>
+<span id="cb45-15"><a href="#cb45-15"></a>  model_no_na <span class="ot">&lt;-</span> <span class="fu">update</span>(</span>
+<span id="cb45-16"><a href="#cb45-16"></a>    model,</span>
+<span id="cb45-17"><a href="#cb45-17"></a>    <span class="at">formula =</span> <span class="fu">terms</span>(model),</span>
+<span id="cb45-18"><a href="#cb45-18"></a>    <span class="at">data =</span> ...data_no_na</span>
+<span id="cb45-19"><a href="#cb45-19"></a>  )</span>
+<span id="cb45-20"><a href="#cb45-20"></a>  </span>
+<span id="cb45-21"><a href="#cb45-21"></a>  <span class="co"># apply step()</span></span>
+<span id="cb45-22"><a href="#cb45-22"></a>  model_simplified <span class="ot">&lt;-</span> <span class="fu">step</span>(model_no_na, ...)</span>
+<span id="cb45-23"><a href="#cb45-23"></a>  </span>
+<span id="cb45-24"><a href="#cb45-24"></a>  <span class="co"># recompute simplified model using full data</span></span>
+<span id="cb45-25"><a href="#cb45-25"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(full_data)) {</span>
+<span id="cb45-26"><a href="#cb45-26"></a>    <span class="fu">update</span>(</span>
+<span id="cb45-27"><a href="#cb45-27"></a>      model,</span>
+<span id="cb45-28"><a href="#cb45-28"></a>      <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified)</span>
+<span id="cb45-29"><a href="#cb45-29"></a>    )</span>
+<span id="cb45-30"><a href="#cb45-30"></a>  } <span class="cf">else</span> {</span>
+<span id="cb45-31"><a href="#cb45-31"></a>    <span class="fu">update</span>(</span>
+<span id="cb45-32"><a href="#cb45-32"></a>      model,</span>
+<span id="cb45-33"><a href="#cb45-33"></a>      <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified),</span>
+<span id="cb45-34"><a href="#cb45-34"></a>      <span class="at">data =</span> full_data</span>
+<span id="cb45-35"><a href="#cb45-35"></a>    )</span>
+<span id="cb45-36"><a href="#cb45-36"></a>  }</span>
+<span id="cb45-37"><a href="#cb45-37"></a>}</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Elle réalise l’ensemble des opérations décrites plus haut en profitant de la flexibilité offerte par la fonction <code><a href="https://rdrr.io/r/stats/update.html">update()</a></code>. La fonction <code><a href="https://rdrr.io/r/stats/model.frame.html">model.frame()</a></code> permet de récupérer le jeu de données utilisé par un modèle (et dans lequel les lignes incomplètes ont été supprimées). La fonction <code><a href="https://rdrr.io/r/stats/terms.html">terms()</a></code> permet de récupérer l’équation du modèle.</p>
 <p><strong>Attention :</strong> il s’agit d’une fonction expérimentale et elle n’est peut-être pas compatible avec tous les types de modèles. Elle a été testée avec les modèles <code><a href="https://rdrr.io/r/stats/lm.html">lm()</a></code>, <code><a href="https://rdrr.io/r/stats/glm.html">glm()</a></code> et <code><a href="https://rdrr.io/pkg/nnet/man/multinom.html">nnet::multinom()</a></code>.</p>
@@ -2446,7 +2462,7 @@ <h1 class="title"><span id="sec-selection-modele-pas-a-pas" class="quarto-sectio
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diff --git a/docs/analyses/selection-modele-pas-a-pas_files/figure-pdf/fig-comparaison-modeles-faceted-1.pdf b/docs/analyses/selection-modele-pas-a-pas_files/figure-pdf/fig-comparaison-modeles-faceted-1.pdf
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 Figure&nbsp;18.1: un histogramme simple
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 Figure&nbsp;18.2: un histogramme personnalisé
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 Figure&nbsp;18.3: un histogramme en 10 classes
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 Figure&nbsp;18.4: une courbe de densité
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 Figure&nbsp;18.5: un diagramme en barres simple
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 Figure&nbsp;18.6: un diagramme en barres épuré
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 Figure&nbsp;18.7: un diagramme en barres épuré
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 Figure&nbsp;40.1: Capture d’écran de l’interface d’<code>explor</code>
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 <span id="cb8-20"><a href="#cb8-20"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </section><section id="représentations-graphiques" class="level3" data-number="40.2.3"><h3 data-number="40.2.3" class="anchored" data-anchor-id="représentations-graphiques">
@@ -781,7 +781,7 @@ <h1 class="title"><span id="sec-analyse-factorielle" class="quarto-section-ident
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 Figure&nbsp;40.2: Représentation graphique de la variance expliquée par les différents axes de l’ACM
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 Figure&nbsp;40.3: Contribution des modalités au premier axe
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 Figure&nbsp;40.4: Contribution des modalités au deuxième axe
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 Figure&nbsp;40.5: Projection des modalités dans le plan factoriel
@@ -874,7 +874,7 @@ <h1 class="title"><span id="sec-analyse-factorielle" class="quarto-section-ident
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 Figure&nbsp;40.6: Projection des individus dans le plan factoriel
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 Figure&nbsp;40.7: Projection des individus dans le plan factoriel
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+<a href="analyse-factorielle_files/figure-html/fig-fviz_mca_ind-habillage-sport-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-10" data-glightbox="description: .lightbox-desc-10"><img src="analyse-factorielle_files/figure-html/fig-fviz_mca_ind-habillage-sport-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-fviz_mca_ind-habillage-sport-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;40.8: Projection des individus dans le plan factoriel selon la pratique d’un sport
@@ -1033,7 +1033,7 @@ <h1 class="title"><span id="sec-analyse-factorielle" class="quarto-section-ident
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+<a href="analyse-factorielle_files/figure-html/fig-fviz_mca_var-acm2_fd-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-12" data-glightbox="description: .lightbox-desc-12"><img src="analyse-factorielle_files/figure-html/fig-fviz_mca_var-acm2_fd-1.png" class="img-fluid figure-img" width="672"></a>
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 Figure&nbsp;40.9: Projection des modalités dans le plan factoriel (incluant les variables additionnelles)
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 Figure&nbsp;40.10: Inertie expliquée par axe (comparaison des ACM réalisées avec ade4 et FactoMineR)
@@ -1808,7 +1808,7 @@ <h1 class="title"><span id="sec-analyse-factorielle" class="quarto-section-ident
 </ul>
 </div>
   </div>
-</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","openEffect":"zoom","loop":false,"closeEffect":"zoom","descPosition":"bottom"});
+</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","openEffect":"zoom","descPosition":"bottom","closeEffect":"zoom","loop":false});
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diff --git a/docs/analyses_avancees/analyse-survie.html b/docs/analyses_avancees/analyse-survie.html
index 63f293f..b966d11 100644
--- a/docs/analyses_avancees/analyse-survie.html
+++ b/docs/analyses_avancees/analyse-survie.html
@@ -333,7 +333,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
@@ -555,7 +555,8 @@
 <li><a href="#visualisation-des-r%C3%A9sultats" id="toc-visualisation-des-résultats" class="nav-link" data-scroll-target="#visualisation-des-r%C3%A9sultats"><span class="header-section-number">42.4.1</span> Visualisation des résultats</a></li>
   <li><a href="#v%C3%A9rification-des-hypoth%C3%A8ses" id="toc-vérification-des-hypothèses" class="nav-link" data-scroll-target="#v%C3%A9rification-des-hypoth%C3%A8ses"><span class="header-section-number">42.4.2</span> Vérification des hypothèses</a></li>
   <li><a href="#repr%C3%A9sentation-graphique-dune-courbe-de-survie-ajust%C3%A9e" id="toc-représentation-graphique-dune-courbe-de-survie-ajustée" class="nav-link" data-scroll-target="#repr%C3%A9sentation-graphique-dune-courbe-de-survie-ajust%C3%A9e"><span class="header-section-number">42.4.3</span> Représentation graphique d’une courbe de survie ajustée</a></li>
-  <li><a href="#mod%C3%A8les-de-cox-univariables" id="toc-modèles-de-cox-univariables" class="nav-link" data-scroll-target="#mod%C3%A8les-de-cox-univariables"><span class="header-section-number">42.4.4</span> Modèles de Cox univariables</a></li>
+  <li><a href="#s%C3%A9lection-pas-%C3%A0-pas-dun-mod%C3%A8le-r%C3%A9duit" id="toc-sélection-pas-à-pas-dun-modèle-réduit" class="nav-link" data-scroll-target="#s%C3%A9lection-pas-%C3%A0-pas-dun-mod%C3%A8le-r%C3%A9duit"><span class="header-section-number">42.4.4</span> Sélection pas à pas d’un modèle réduit</a></li>
+  <li><a href="#mod%C3%A8les-de-cox-univariables" id="toc-modèles-de-cox-univariables" class="nav-link" data-scroll-target="#mod%C3%A8les-de-cox-univariables"><span class="header-section-number">42.4.5</span> Modèles de Cox univariables</a></li>
   </ul>
 </li>
   <li><a href="#analyse-de-survie-pond%C3%A9r%C3%A9e" id="toc-analyse-de-survie-pondérée" class="nav-link" data-scroll-target="#analyse-de-survie-pond%C3%A9r%C3%A9e"><span class="header-section-number">42.5</span> Analyse de survie pondérée</a></li>
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+<div id="ttjevpuneb" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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-#ptdxxnqrny thead, #ptdxxnqrny tbody, #ptdxxnqrny tfoot, #ptdxxnqrny tr, #ptdxxnqrny td, #ptdxxnqrny th {
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   font-size: 125%;
   font-weight: initial;
@@ -1242,7 +1243,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#ptdxxnqrny .gt_subtitle {
+#ttjevpuneb .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1254,7 +1255,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#ptdxxnqrny .gt_heading {
+#ttjevpuneb .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1266,13 +1267,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_bottom_border {
+#ttjevpuneb .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_col_headings {
+#ttjevpuneb .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1287,7 +1288,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_col_heading {
+#ttjevpuneb .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1307,7 +1308,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#ptdxxnqrny .gt_column_spanner_outer {
+#ttjevpuneb .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1319,15 +1320,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#ptdxxnqrny .gt_column_spanner_outer:first-child {
+#ttjevpuneb .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#ptdxxnqrny .gt_column_spanner_outer:last-child {
+#ttjevpuneb .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#ptdxxnqrny .gt_column_spanner {
+#ttjevpuneb .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1339,11 +1340,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#ptdxxnqrny .gt_spanner_row {
+#ttjevpuneb .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#ptdxxnqrny .gt_group_heading {
+#ttjevpuneb .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1369,7 +1370,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#ptdxxnqrny .gt_empty_group_heading {
+#ttjevpuneb .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1384,15 +1385,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#ptdxxnqrny .gt_from_md > :first-child {
+#ttjevpuneb .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#ptdxxnqrny .gt_from_md > :last-child {
+#ttjevpuneb .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#ptdxxnqrny .gt_row {
+#ttjevpuneb .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1411,7 +1412,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#ptdxxnqrny .gt_stub {
+#ttjevpuneb .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1424,7 +1425,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ptdxxnqrny .gt_stub_row_group {
+#ttjevpuneb .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1438,15 +1439,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: top;
 }
 
-#ptdxxnqrny .gt_row_group_first td {
+#ttjevpuneb .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#ptdxxnqrny .gt_row_group_first th {
+#ttjevpuneb .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#ptdxxnqrny .gt_summary_row {
+#ttjevpuneb .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1456,16 +1457,16 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ptdxxnqrny .gt_first_summary_row {
+#ttjevpuneb .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_first_summary_row.thick {
+#ttjevpuneb .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#ptdxxnqrny .gt_last_summary_row {
+#ttjevpuneb .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1475,7 +1476,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_grand_summary_row {
+#ttjevpuneb .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -1485,7 +1486,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ptdxxnqrny .gt_first_grand_summary_row {
+#ttjevpuneb .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1495,7 +1496,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_last_grand_summary_row_top {
+#ttjevpuneb .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1505,11 +1506,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_striped {
+#ttjevpuneb .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#ptdxxnqrny .gt_table_body {
+#ttjevpuneb .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1518,7 +1519,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_footnotes {
+#ttjevpuneb .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1532,7 +1533,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_footnote {
+#ttjevpuneb .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -1541,7 +1542,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ptdxxnqrny .gt_sourcenotes {
+#ttjevpuneb .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -1555,7 +1556,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ptdxxnqrny .gt_sourcenote {
+#ttjevpuneb .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -1563,72 +1564,72 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ptdxxnqrny .gt_left {
+#ttjevpuneb .gt_left {
   text-align: left;
 }
 
-#ptdxxnqrny .gt_center {
+#ttjevpuneb .gt_center {
   text-align: center;
 }
 
-#ptdxxnqrny .gt_right {
+#ttjevpuneb .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#ptdxxnqrny .gt_font_normal {
+#ttjevpuneb .gt_font_normal {
   font-weight: normal;
 }
 
-#ptdxxnqrny .gt_font_bold {
+#ttjevpuneb .gt_font_bold {
   font-weight: bold;
 }
 
-#ptdxxnqrny .gt_font_italic {
+#ttjevpuneb .gt_font_italic {
   font-style: italic;
 }
 
-#ptdxxnqrny .gt_super {
+#ttjevpuneb .gt_super {
   font-size: 65%;
 }
 
-#ptdxxnqrny .gt_footnote_marks {
+#ttjevpuneb .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#ptdxxnqrny .gt_asterisk {
+#ttjevpuneb .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#ptdxxnqrny .gt_indent_1 {
+#ttjevpuneb .gt_indent_1 {
   text-indent: 5px;
 }
 
-#ptdxxnqrny .gt_indent_2 {
+#ttjevpuneb .gt_indent_2 {
   text-indent: 10px;
 }
 
-#ptdxxnqrny .gt_indent_3 {
+#ttjevpuneb .gt_indent_3 {
   text-indent: 15px;
 }
 
-#ptdxxnqrny .gt_indent_4 {
+#ttjevpuneb .gt_indent_4 {
   text-indent: 20px;
 }
 
-#ptdxxnqrny .gt_indent_5 {
+#ttjevpuneb .gt_indent_5 {
   text-indent: 25px;
 }
 
-#ptdxxnqrny .katex-display {
+#ttjevpuneb .katex-display {
   display: inline-flex !important;
   margin-bottom: 0.75em !important;
 }
 
-#ptdxxnqrny div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+#ttjevpuneb div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
   height: 0px !important;
 }
 </style>
@@ -1742,23 +1743,23 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 <span id="cb17-4"><a href="#cb17-4"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="tsmhgslupm" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#tsmhgslupm table {
+<div id="gwrkdybqza" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#gwrkdybqza table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#tsmhgslupm thead, #tsmhgslupm tbody, #tsmhgslupm tfoot, #tsmhgslupm tr, #tsmhgslupm td, #tsmhgslupm th {
+#gwrkdybqza thead, #gwrkdybqza tbody, #gwrkdybqza tfoot, #gwrkdybqza tr, #gwrkdybqza td, #gwrkdybqza th {
   border-style: none;
 }
 
-#tsmhgslupm p {
+#gwrkdybqza p {
   margin: 0;
   padding: 0;
 }
 
-#tsmhgslupm .gt_table {
+#gwrkdybqza .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -1784,12 +1785,12 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-left-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_caption {
+#gwrkdybqza .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#tsmhgslupm .gt_title {
+#gwrkdybqza .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -1801,7 +1802,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#tsmhgslupm .gt_subtitle {
+#gwrkdybqza .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -1813,7 +1814,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#tsmhgslupm .gt_heading {
+#gwrkdybqza .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -1825,13 +1826,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_bottom_border {
+#gwrkdybqza .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_col_headings {
+#gwrkdybqza .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -1846,7 +1847,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_col_heading {
+#gwrkdybqza .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1866,7 +1867,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#tsmhgslupm .gt_column_spanner_outer {
+#gwrkdybqza .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1878,15 +1879,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#tsmhgslupm .gt_column_spanner_outer:first-child {
+#gwrkdybqza .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#tsmhgslupm .gt_column_spanner_outer:last-child {
+#gwrkdybqza .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#tsmhgslupm .gt_column_spanner {
+#gwrkdybqza .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -1898,11 +1899,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#tsmhgslupm .gt_spanner_row {
+#gwrkdybqza .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#tsmhgslupm .gt_group_heading {
+#gwrkdybqza .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1928,7 +1929,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#tsmhgslupm .gt_empty_group_heading {
+#gwrkdybqza .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -1943,15 +1944,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#tsmhgslupm .gt_from_md > :first-child {
+#gwrkdybqza .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#tsmhgslupm .gt_from_md > :last-child {
+#gwrkdybqza .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#tsmhgslupm .gt_row {
+#gwrkdybqza .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -1970,7 +1971,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#tsmhgslupm .gt_stub {
+#gwrkdybqza .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1983,7 +1984,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#tsmhgslupm .gt_stub_row_group {
+#gwrkdybqza .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -1997,15 +1998,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: top;
 }
 
-#tsmhgslupm .gt_row_group_first td {
+#gwrkdybqza .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#tsmhgslupm .gt_row_group_first th {
+#gwrkdybqza .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#tsmhgslupm .gt_summary_row {
+#gwrkdybqza .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2015,16 +2016,16 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#tsmhgslupm .gt_first_summary_row {
+#gwrkdybqza .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_first_summary_row.thick {
+#gwrkdybqza .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#tsmhgslupm .gt_last_summary_row {
+#gwrkdybqza .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2034,7 +2035,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_grand_summary_row {
+#gwrkdybqza .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2044,7 +2045,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#tsmhgslupm .gt_first_grand_summary_row {
+#gwrkdybqza .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2054,7 +2055,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_last_grand_summary_row_top {
+#gwrkdybqza .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2064,11 +2065,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_striped {
+#gwrkdybqza .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#tsmhgslupm .gt_table_body {
+#gwrkdybqza .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2077,7 +2078,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_footnotes {
+#gwrkdybqza .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2091,7 +2092,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_footnote {
+#gwrkdybqza .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2100,7 +2101,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#tsmhgslupm .gt_sourcenotes {
+#gwrkdybqza .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2114,7 +2115,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#tsmhgslupm .gt_sourcenote {
+#gwrkdybqza .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2122,72 +2123,72 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#tsmhgslupm .gt_left {
+#gwrkdybqza .gt_left {
   text-align: left;
 }
 
-#tsmhgslupm .gt_center {
+#gwrkdybqza .gt_center {
   text-align: center;
 }
 
-#tsmhgslupm .gt_right {
+#gwrkdybqza .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#tsmhgslupm .gt_font_normal {
+#gwrkdybqza .gt_font_normal {
   font-weight: normal;
 }
 
-#tsmhgslupm .gt_font_bold {
+#gwrkdybqza .gt_font_bold {
   font-weight: bold;
 }
 
-#tsmhgslupm .gt_font_italic {
+#gwrkdybqza .gt_font_italic {
   font-style: italic;
 }
 
-#tsmhgslupm .gt_super {
+#gwrkdybqza .gt_super {
   font-size: 65%;
 }
 
-#tsmhgslupm .gt_footnote_marks {
+#gwrkdybqza .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#tsmhgslupm .gt_asterisk {
+#gwrkdybqza .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#tsmhgslupm .gt_indent_1 {
+#gwrkdybqza .gt_indent_1 {
   text-indent: 5px;
 }
 
-#tsmhgslupm .gt_indent_2 {
+#gwrkdybqza .gt_indent_2 {
   text-indent: 10px;
 }
 
-#tsmhgslupm .gt_indent_3 {
+#gwrkdybqza .gt_indent_3 {
   text-indent: 15px;
 }
 
-#tsmhgslupm .gt_indent_4 {
+#gwrkdybqza .gt_indent_4 {
   text-indent: 20px;
 }
 
-#tsmhgslupm .gt_indent_5 {
+#gwrkdybqza .gt_indent_5 {
   text-indent: 25px;
 }
 
-#tsmhgslupm .katex-display {
+#gwrkdybqza .katex-display {
   display: inline-flex !important;
   margin-bottom: 0.75em !important;
 }
 
-#tsmhgslupm div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+#gwrkdybqza div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
   height: 0px !important;
 }
 </style>
@@ -2266,23 +2267,23 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 <span id="cb18-8"><a href="#cb18-8"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="bacxdwpdad" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#bacxdwpdad table {
+<div id="fwifttizdl" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#fwifttizdl table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#bacxdwpdad thead, #bacxdwpdad tbody, #bacxdwpdad tfoot, #bacxdwpdad tr, #bacxdwpdad td, #bacxdwpdad th {
+#fwifttizdl thead, #fwifttizdl tbody, #fwifttizdl tfoot, #fwifttizdl tr, #fwifttizdl td, #fwifttizdl th {
   border-style: none;
 }
 
-#bacxdwpdad p {
+#fwifttizdl p {
   margin: 0;
   padding: 0;
 }
 
-#bacxdwpdad .gt_table {
+#fwifttizdl .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2308,12 +2309,12 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-left-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_caption {
+#fwifttizdl .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#bacxdwpdad .gt_title {
+#fwifttizdl .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2325,7 +2326,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#bacxdwpdad .gt_subtitle {
+#fwifttizdl .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2337,7 +2338,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#bacxdwpdad .gt_heading {
+#fwifttizdl .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2349,13 +2350,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_bottom_border {
+#fwifttizdl .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_col_headings {
+#fwifttizdl .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2370,7 +2371,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_col_heading {
+#fwifttizdl .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2390,7 +2391,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#bacxdwpdad .gt_column_spanner_outer {
+#fwifttizdl .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2402,15 +2403,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#bacxdwpdad .gt_column_spanner_outer:first-child {
+#fwifttizdl .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#bacxdwpdad .gt_column_spanner_outer:last-child {
+#fwifttizdl .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#bacxdwpdad .gt_column_spanner {
+#fwifttizdl .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -2422,11 +2423,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#bacxdwpdad .gt_spanner_row {
+#fwifttizdl .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#bacxdwpdad .gt_group_heading {
+#fwifttizdl .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2452,7 +2453,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#bacxdwpdad .gt_empty_group_heading {
+#fwifttizdl .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -2467,15 +2468,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#bacxdwpdad .gt_from_md > :first-child {
+#fwifttizdl .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#bacxdwpdad .gt_from_md > :last-child {
+#fwifttizdl .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#bacxdwpdad .gt_row {
+#fwifttizdl .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2494,7 +2495,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#bacxdwpdad .gt_stub {
+#fwifttizdl .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2507,7 +2508,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#bacxdwpdad .gt_stub_row_group {
+#fwifttizdl .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2521,15 +2522,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: top;
 }
 
-#bacxdwpdad .gt_row_group_first td {
+#fwifttizdl .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#bacxdwpdad .gt_row_group_first th {
+#fwifttizdl .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#bacxdwpdad .gt_summary_row {
+#fwifttizdl .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2539,16 +2540,16 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#bacxdwpdad .gt_first_summary_row {
+#fwifttizdl .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_first_summary_row.thick {
+#fwifttizdl .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#bacxdwpdad .gt_last_summary_row {
+#fwifttizdl .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2558,7 +2559,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_grand_summary_row {
+#fwifttizdl .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -2568,7 +2569,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#bacxdwpdad .gt_first_grand_summary_row {
+#fwifttizdl .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2578,7 +2579,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_last_grand_summary_row_top {
+#fwifttizdl .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -2588,11 +2589,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_striped {
+#fwifttizdl .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#bacxdwpdad .gt_table_body {
+#fwifttizdl .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2601,7 +2602,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_footnotes {
+#fwifttizdl .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2615,7 +2616,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_footnote {
+#fwifttizdl .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -2624,7 +2625,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#bacxdwpdad .gt_sourcenotes {
+#fwifttizdl .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -2638,7 +2639,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#bacxdwpdad .gt_sourcenote {
+#fwifttizdl .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -2646,72 +2647,72 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#bacxdwpdad .gt_left {
+#fwifttizdl .gt_left {
   text-align: left;
 }
 
-#bacxdwpdad .gt_center {
+#fwifttizdl .gt_center {
   text-align: center;
 }
 
-#bacxdwpdad .gt_right {
+#fwifttizdl .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#bacxdwpdad .gt_font_normal {
+#fwifttizdl .gt_font_normal {
   font-weight: normal;
 }
 
-#bacxdwpdad .gt_font_bold {
+#fwifttizdl .gt_font_bold {
   font-weight: bold;
 }
 
-#bacxdwpdad .gt_font_italic {
+#fwifttizdl .gt_font_italic {
   font-style: italic;
 }
 
-#bacxdwpdad .gt_super {
+#fwifttizdl .gt_super {
   font-size: 65%;
 }
 
-#bacxdwpdad .gt_footnote_marks {
+#fwifttizdl .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#bacxdwpdad .gt_asterisk {
+#fwifttizdl .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#bacxdwpdad .gt_indent_1 {
+#fwifttizdl .gt_indent_1 {
   text-indent: 5px;
 }
 
-#bacxdwpdad .gt_indent_2 {
+#fwifttizdl .gt_indent_2 {
   text-indent: 10px;
 }
 
-#bacxdwpdad .gt_indent_3 {
+#fwifttizdl .gt_indent_3 {
   text-indent: 15px;
 }
 
-#bacxdwpdad .gt_indent_4 {
+#fwifttizdl .gt_indent_4 {
   text-indent: 20px;
 }
 
-#bacxdwpdad .gt_indent_5 {
+#fwifttizdl .gt_indent_5 {
   text-indent: 25px;
 }
 
-#bacxdwpdad .katex-display {
+#fwifttizdl .katex-display {
   display: inline-flex !important;
   margin-bottom: 0.75em !important;
 }
 
-#bacxdwpdad div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+#fwifttizdl div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
   height: 0px !important;
 }
 </style>
@@ -2817,23 +2818,23 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 <span id="cb20-4"><a href="#cb20-4"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<div id="clfaovqeub" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#clfaovqeub table {
+<div id="zsyjrjgkwx" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#zsyjrjgkwx table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#clfaovqeub thead, #clfaovqeub tbody, #clfaovqeub tfoot, #clfaovqeub tr, #clfaovqeub td, #clfaovqeub th {
+#zsyjrjgkwx thead, #zsyjrjgkwx tbody, #zsyjrjgkwx tfoot, #zsyjrjgkwx tr, #zsyjrjgkwx td, #zsyjrjgkwx th {
   border-style: none;
 }
 
-#clfaovqeub p {
+#zsyjrjgkwx p {
   margin: 0;
   padding: 0;
 }
 
-#clfaovqeub .gt_table {
+#zsyjrjgkwx .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -2859,12 +2860,12 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-left-color: #D3D3D3;
 }
 
-#clfaovqeub .gt_caption {
+#zsyjrjgkwx .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#clfaovqeub .gt_title {
+#zsyjrjgkwx .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -2876,7 +2877,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#clfaovqeub .gt_subtitle {
+#zsyjrjgkwx .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -2888,7 +2889,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#clfaovqeub .gt_heading {
+#zsyjrjgkwx .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -2900,13 +2901,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#clfaovqeub .gt_bottom_border {
+#zsyjrjgkwx .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#clfaovqeub .gt_col_headings {
+#zsyjrjgkwx .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -2921,7 +2922,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#clfaovqeub .gt_col_heading {
+#zsyjrjgkwx .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2941,7 +2942,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#clfaovqeub .gt_column_spanner_outer {
+#zsyjrjgkwx .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -2953,15 +2954,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#clfaovqeub .gt_column_spanner_outer:first-child {
+#zsyjrjgkwx .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#clfaovqeub .gt_column_spanner_outer:last-child {
+#zsyjrjgkwx .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#clfaovqeub .gt_column_spanner {
+#zsyjrjgkwx .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -2973,11 +2974,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#clfaovqeub .gt_spanner_row {
+#zsyjrjgkwx .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#clfaovqeub .gt_group_heading {
+#zsyjrjgkwx .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3003,7 +3004,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#clfaovqeub .gt_empty_group_heading {
+#zsyjrjgkwx .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -3018,15 +3019,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#clfaovqeub .gt_from_md > :first-child {
+#zsyjrjgkwx .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#clfaovqeub .gt_from_md > :last-child {
+#zsyjrjgkwx .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#clfaovqeub .gt_row {
+#zsyjrjgkwx .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3045,7 +3046,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#clfaovqeub .gt_stub {
+#zsyjrjgkwx .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3058,7 +3059,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#clfaovqeub .gt_stub_row_group {
+#zsyjrjgkwx .gt_stub_row_group {
   color: #333333;
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@@ -3072,15 +3073,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
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 }
 
-#clfaovqeub .gt_row_group_first td {
+#zsyjrjgkwx .gt_row_group_first td {
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 }
 
-#clfaovqeub .gt_grand_summary_row {
+#zsyjrjgkwx .gt_grand_summary_row {
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 }
 
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+#zsyjrjgkwx .gt_footnotes {
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 }
 
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 }
 
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 }
 
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+#zsyjrjgkwx .gt_font_bold {
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+#zsyjrjgkwx .katex-display {
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-#clfaovqeub div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+#zsyjrjgkwx div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
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 }
 </style>
@@ -3420,37 +3421,154 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </div>
 </div>
 </div>
-</section><section id="modèles-de-cox-univariables" class="level3" data-number="42.4.4"><h3 data-number="42.4.4" class="anchored" data-anchor-id="modèles-de-cox-univariables">
-<span class="header-section-number">42.4.4</span> Modèles de Cox univariables</h3>
+</section><section id="sélection-pas-à-pas-dun-modèle-réduit" class="level3" data-number="42.4.4"><h3 data-number="42.4.4" class="anchored" data-anchor-id="sélection-pas-à-pas-dun-modèle-réduit">
+<span class="header-section-number">42.4.4</span> Sélection pas à pas d’un modèle réduit</h3>
+<p>Pour réduire le modèle (voir <a href="../analyses/selection-modele-pas-a-pas.html" class="quarto-xref"><span>Chapitre 23</span></a>), il est possible d’utiliser la fonction <code><a href="https://rdrr.io/r/stats/step.html">step()</a></code>.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a>mod <span class="sc">|&gt;</span> <span class="fu">step</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Start:  AIC=1002.04
+Surv(ttdeath, death) ~ stage + trt + age</code></pre>
+</div>
+<div class="cell-output cell-output-error">
+<pre><code>Error in drop1.default(fit, scope$drop, scale = scale, trace = trace, : le nombre de lignes utilisées a changé : supprimer les valeurs manquantes ?</code></pre>
+</div>
+</div>
+<p>Dans notre exemple, nous rencontrons une erreur liée à des valeurs manquantes pour l’une des variables explicatives. Comme abordé précédemment (cf. <a href="../analyses/selection-modele-pas-a-pas.html#sec-step-avec-na" class="quarto-xref"><span>Section 23.8</span></a>), nous pouvons avoir recours à la fonction <code>step_with_na()</code> de notre cru.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a>step_with_na <span class="ot">&lt;-</span> <span class="cf">function</span>(model,</span>
+<span id="cb30-2"><a href="#cb30-2"></a>                         <span class="at">full_data =</span> <span class="cn">NULL</span>, ...) {</span>
+<span id="cb30-3"><a href="#cb30-3"></a>  <span class="co"># data with no NAs</span></span>
+<span id="cb30-4"><a href="#cb30-4"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(full_data)) {</span>
+<span id="cb30-5"><a href="#cb30-5"></a>    ...data_no_na <span class="ot">&lt;-</span> <span class="fu">model.frame</span>(model)</span>
+<span id="cb30-6"><a href="#cb30-6"></a>  } <span class="cf">else</span> {</span>
+<span id="cb30-7"><a href="#cb30-7"></a>    ...data_no_na <span class="ot">&lt;-</span> <span class="fu">get_all_vars</span>(model, <span class="at">data =</span> full_data)</span>
+<span id="cb30-8"><a href="#cb30-8"></a>    ...data_no_na <span class="ot">&lt;-</span> ...data_no_na[<span class="fu">complete.cases</span>(...data_no_na), ]</span>
+<span id="cb30-9"><a href="#cb30-9"></a>  }</span>
+<span id="cb30-10"><a href="#cb30-10"></a>  </span>
+<span id="cb30-11"><a href="#cb30-11"></a>  <span class="co"># assign ...data_no_na in parent.frame()</span></span>
+<span id="cb30-12"><a href="#cb30-12"></a>  <span class="fu">assign</span>(<span class="st">"...data_no_na"</span>, ...data_no_na, <span class="at">envir =</span> <span class="fu">parent.frame</span>())</span>
+<span id="cb30-13"><a href="#cb30-13"></a>  </span>
+<span id="cb30-14"><a href="#cb30-14"></a>  <span class="co"># refit the model without NAs</span></span>
+<span id="cb30-15"><a href="#cb30-15"></a>  model_no_na <span class="ot">&lt;-</span> <span class="fu">update</span>(</span>
+<span id="cb30-16"><a href="#cb30-16"></a>    model,</span>
+<span id="cb30-17"><a href="#cb30-17"></a>    <span class="at">formula =</span> <span class="fu">terms</span>(model),</span>
+<span id="cb30-18"><a href="#cb30-18"></a>    <span class="at">data =</span> ...data_no_na</span>
+<span id="cb30-19"><a href="#cb30-19"></a>  )</span>
+<span id="cb30-20"><a href="#cb30-20"></a>  </span>
+<span id="cb30-21"><a href="#cb30-21"></a>  <span class="co"># apply step()</span></span>
+<span id="cb30-22"><a href="#cb30-22"></a>  model_simplified <span class="ot">&lt;-</span> <span class="fu">step</span>(model_no_na, ...)</span>
+<span id="cb30-23"><a href="#cb30-23"></a>  </span>
+<span id="cb30-24"><a href="#cb30-24"></a>  <span class="co"># recompute simplified model using full data</span></span>
+<span id="cb30-25"><a href="#cb30-25"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(full_data)) {</span>
+<span id="cb30-26"><a href="#cb30-26"></a>    <span class="fu">update</span>(</span>
+<span id="cb30-27"><a href="#cb30-27"></a>      model,</span>
+<span id="cb30-28"><a href="#cb30-28"></a>      <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified)</span>
+<span id="cb30-29"><a href="#cb30-29"></a>    )</span>
+<span id="cb30-30"><a href="#cb30-30"></a>  } <span class="cf">else</span> {</span>
+<span id="cb30-31"><a href="#cb30-31"></a>    <span class="fu">update</span>(</span>
+<span id="cb30-32"><a href="#cb30-32"></a>      model,</span>
+<span id="cb30-33"><a href="#cb30-33"></a>      <span class="at">formula =</span> <span class="fu">terms</span>(model_simplified),</span>
+<span id="cb30-34"><a href="#cb30-34"></a>      <span class="at">data =</span> full_data</span>
+<span id="cb30-35"><a href="#cb30-35"></a>    )</span>
+<span id="cb30-36"><a href="#cb30-36"></a>  }</span>
+<span id="cb30-37"><a href="#cb30-37"></a>}</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>Il y a néanmoins une petite subtilité pour l’utiliser ici. En effet, elle a besoin de pouvoir reconstruire notre outcome qui est ici une variable composée créée avec <code>Surv()</code>. Deux possibilités. La première consiste à transmettre à <code>step_with_na()</code> le jeu de données complet.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a>mod <span class="sc">|&gt;</span> <span class="fu">step_with_na</span>(<span class="at">full_data =</span> trial)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Start:  AIC=1002.04
+Surv(ttdeath, death) ~ stage + trt + age
+
+        Df    AIC
+&lt;none&gt;     1002.0
+- age    1 1002.1
+- trt    1 1002.5
+- stage  3 1011.2</code></pre>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Call:
+coxph(formula = Surv(ttdeath, death) ~ stage + trt + age, data = full_data)
+
+              coef exp(coef) se(coef)     z        p
+stageT2   0.268596  1.308126 0.287412 0.935 0.350028
+stageT3   0.164980  1.179370 0.309897 0.532 0.594468
+stageT4   1.000324  2.719163 0.276830 3.614 0.000302
+trtDrug B 0.312336  1.366614 0.199005 1.569 0.116535
+age       0.010305  1.010358 0.007154 1.440 0.149753
+
+Likelihood ratio test=17.98  on 5 df, p=0.002975
+n= 189, number of events= 103 
+   (11 observations effacées parce que manquantes)</code></pre>
+</div>
+</div>
+<p>La seconde consiste à créer notre outcome en amont du calcul du modèle.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a>trial <span class="ot">&lt;-</span> trial <span class="sc">|&gt;</span> </span>
+<span id="cb34-2"><a href="#cb34-2"></a>  <span class="fu">mutate</span>(<span class="at">survie =</span> <span class="fu">Surv</span>(ttdeath, death))</span>
+<span id="cb34-3"><a href="#cb34-3"></a>mod2 <span class="ot">&lt;-</span> <span class="fu">coxph</span>(</span>
+<span id="cb34-4"><a href="#cb34-4"></a>  survie <span class="sc">~</span> stage <span class="sc">+</span> trt <span class="sc">+</span> age,</span>
+<span id="cb34-5"><a href="#cb34-5"></a>  <span class="at">data =</span> trial</span>
+<span id="cb34-6"><a href="#cb34-6"></a>)</span>
+<span id="cb34-7"><a href="#cb34-7"></a>mod2 <span class="sc">|&gt;</span> <span class="fu">step_with_na</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Start:  AIC=1002.04
+survie ~ stage + trt + age
+
+        Df    AIC
+&lt;none&gt;     1002.0
+- age    1 1002.1
+- trt    1 1002.5
+- stage  3 1011.2</code></pre>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>Call:
+coxph(formula = survie ~ stage + trt + age, data = trial)
+
+              coef exp(coef) se(coef)     z        p
+stageT2   0.268596  1.308126 0.287412 0.935 0.350028
+stageT3   0.164980  1.179370 0.309897 0.532 0.594468
+stageT4   1.000324  2.719163 0.276830 3.614 0.000302
+trtDrug B 0.312336  1.366614 0.199005 1.569 0.116535
+age       0.010305  1.010358 0.007154 1.440 0.149753
+
+Likelihood ratio test=17.98  on 5 df, p=0.002975
+n= 189, number of events= 103 
+   (11 observations effacées parce que manquantes)</code></pre>
+</div>
+</div>
+</section><section id="modèles-de-cox-univariables" class="level3" data-number="42.4.5"><h3 data-number="42.4.5" class="anchored" data-anchor-id="modèles-de-cox-univariables">
+<span class="header-section-number">42.4.5</span> Modèles de Cox univariables</h3>
 <p>Comme déjà abordé dans le chapitre sur la régression logistique binaire, il est possible d’effectuer en une fois plusieurs modèles univariables avec <code><a href="https://www.danieldsjoberg.com/gtsummary/reference/tbl_uvregression.html">gtsummary::tbl_uvregression()</a></code> (cf. <a href="../analyses/regression-logistique-binaire.html#sec-regressions-logistiques-univariables" class="quarto-xref"><span>Section 22.9</span></a>).</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a>trial <span class="sc">|&gt;</span> </span>
-<span id="cb27-2"><a href="#cb27-2"></a>  <span class="fu">tbl_uvregression</span>(</span>
-<span id="cb27-3"><a href="#cb27-3"></a>    <span class="at">method =</span> coxph,</span>
-<span id="cb27-4"><a href="#cb27-4"></a>    <span class="at">y =</span> <span class="fu">Surv</span>(ttdeath, death),</span>
-<span id="cb27-5"><a href="#cb27-5"></a>    <span class="at">include =</span> <span class="fu">c</span>(stage, trt, age),</span>
-<span id="cb27-6"><a href="#cb27-6"></a>    <span class="at">exponentiate =</span> <span class="cn">TRUE</span></span>
-<span id="cb27-7"><a href="#cb27-7"></a>  ) <span class="sc">|&gt;</span> </span>
-<span id="cb27-8"><a href="#cb27-8"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1"></a>trial <span class="sc">|&gt;</span> </span>
+<span id="cb37-2"><a href="#cb37-2"></a>  <span class="fu">tbl_uvregression</span>(</span>
+<span id="cb37-3"><a href="#cb37-3"></a>    <span class="at">method =</span> coxph,</span>
+<span id="cb37-4"><a href="#cb37-4"></a>    <span class="at">y =</span> <span class="fu">Surv</span>(ttdeath, death),</span>
+<span id="cb37-5"><a href="#cb37-5"></a>    <span class="at">include =</span> <span class="fu">c</span>(stage, trt, age),</span>
+<span id="cb37-6"><a href="#cb37-6"></a>    <span class="at">exponentiate =</span> <span class="cn">TRUE</span></span>
+<span id="cb37-7"><a href="#cb37-7"></a>  ) <span class="sc">|&gt;</span> </span>
+<span id="cb37-8"><a href="#cb37-8"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
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   border-left-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_caption {
+#aowibjnhhx .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#yigmnrsixj .gt_title {
+#aowibjnhhx .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -3493,7 +3611,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#yigmnrsixj .gt_subtitle {
+#aowibjnhhx .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -3505,7 +3623,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#yigmnrsixj .gt_heading {
+#aowibjnhhx .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -3517,13 +3635,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_bottom_border {
+#aowibjnhhx .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_col_headings {
+#aowibjnhhx .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -3538,7 +3656,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_col_heading {
+#aowibjnhhx .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3558,7 +3676,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#yigmnrsixj .gt_column_spanner_outer {
+#aowibjnhhx .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3570,15 +3688,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#yigmnrsixj .gt_column_spanner_outer:first-child {
+#aowibjnhhx .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#yigmnrsixj .gt_column_spanner_outer:last-child {
+#aowibjnhhx .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#yigmnrsixj .gt_column_spanner {
+#aowibjnhhx .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -3590,11 +3708,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#yigmnrsixj .gt_spanner_row {
+#aowibjnhhx .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#yigmnrsixj .gt_group_heading {
+#aowibjnhhx .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3620,7 +3738,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#yigmnrsixj .gt_empty_group_heading {
+#aowibjnhhx .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -3635,15 +3753,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#yigmnrsixj .gt_from_md > :first-child {
+#aowibjnhhx .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#yigmnrsixj .gt_from_md > :last-child {
+#aowibjnhhx .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#yigmnrsixj .gt_row {
+#aowibjnhhx .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3662,7 +3780,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#yigmnrsixj .gt_stub {
+#aowibjnhhx .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3675,7 +3793,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#yigmnrsixj .gt_stub_row_group {
+#aowibjnhhx .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -3689,15 +3807,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: top;
 }
 
-#yigmnrsixj .gt_row_group_first td {
+#aowibjnhhx .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#yigmnrsixj .gt_row_group_first th {
+#aowibjnhhx .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#yigmnrsixj .gt_summary_row {
+#aowibjnhhx .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3707,16 +3825,16 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#yigmnrsixj .gt_first_summary_row {
+#aowibjnhhx .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_first_summary_row.thick {
+#aowibjnhhx .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#yigmnrsixj .gt_last_summary_row {
+#aowibjnhhx .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3726,7 +3844,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_grand_summary_row {
+#aowibjnhhx .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -3736,7 +3854,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#yigmnrsixj .gt_first_grand_summary_row {
+#aowibjnhhx .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3746,7 +3864,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_last_grand_summary_row_top {
+#aowibjnhhx .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -3756,11 +3874,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_striped {
+#aowibjnhhx .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#yigmnrsixj .gt_table_body {
+#aowibjnhhx .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -3769,7 +3887,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_footnotes {
+#aowibjnhhx .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3783,7 +3901,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_footnote {
+#aowibjnhhx .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -3792,7 +3910,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#yigmnrsixj .gt_sourcenotes {
+#aowibjnhhx .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -3806,7 +3924,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#yigmnrsixj .gt_sourcenote {
+#aowibjnhhx .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -3814,72 +3932,72 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#yigmnrsixj .gt_left {
+#aowibjnhhx .gt_left {
   text-align: left;
 }
 
-#yigmnrsixj .gt_center {
+#aowibjnhhx .gt_center {
   text-align: center;
 }
 
-#yigmnrsixj .gt_right {
+#aowibjnhhx .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#yigmnrsixj .gt_font_normal {
+#aowibjnhhx .gt_font_normal {
   font-weight: normal;
 }
 
-#yigmnrsixj .gt_font_bold {
+#aowibjnhhx .gt_font_bold {
   font-weight: bold;
 }
 
-#yigmnrsixj .gt_font_italic {
+#aowibjnhhx .gt_font_italic {
   font-style: italic;
 }
 
-#yigmnrsixj .gt_super {
+#aowibjnhhx .gt_super {
   font-size: 65%;
 }
 
-#yigmnrsixj .gt_footnote_marks {
+#aowibjnhhx .gt_footnote_marks {
   font-size: 75%;
   vertical-align: 0.4em;
   position: initial;
 }
 
-#yigmnrsixj .gt_asterisk {
+#aowibjnhhx .gt_asterisk {
   font-size: 100%;
   vertical-align: 0;
 }
 
-#yigmnrsixj .gt_indent_1 {
+#aowibjnhhx .gt_indent_1 {
   text-indent: 5px;
 }
 
-#yigmnrsixj .gt_indent_2 {
+#aowibjnhhx .gt_indent_2 {
   text-indent: 10px;
 }
 
-#yigmnrsixj .gt_indent_3 {
+#aowibjnhhx .gt_indent_3 {
   text-indent: 15px;
 }
 
-#yigmnrsixj .gt_indent_4 {
+#aowibjnhhx .gt_indent_4 {
   text-indent: 20px;
 }
 
-#yigmnrsixj .gt_indent_5 {
+#aowibjnhhx .gt_indent_5 {
   text-indent: 25px;
 }
 
-#yigmnrsixj .katex-display {
+#aowibjnhhx .katex-display {
   display: inline-flex !important;
   margin-bottom: 0.75em !important;
 }
 
-#yigmnrsixj div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
+#aowibjnhhx div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
   height: 0px !important;
 }
 </style>
@@ -3990,7 +4108,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 <p>Lorsque l’on travaille avec des données pondérées et un plan d’échantillonnage complexe, le plus pertinent est d’avoir recours au package <a href="http://r-survey.r-forge.r-project.org/survey/">survey</a> (cf. <a href="../donnees_ponderees/plan-echantillonnage.html" class="quarto-xref"><span>Chapitre 28</span></a>) qui propose des fonctions adaptées à l’analyse de survie.</p>
 <p>Pour l’exemple, nous allons transformer le jeu de données en objet <code>survey</code> en indiquant simplement des poids uniformes égaux à 1.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a><span class="fu">library</span>(survey)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1"></a><span class="fu">library</span>(survey)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
 <pre><code>Le chargement a nécessité le package : grid</code></pre>
 </div>
@@ -4006,7 +4124,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 
     dotchart</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1"></a>strial <span class="ot">&lt;-</span> <span class="fu">svydesign</span>(<span class="sc">~</span> <span class="dv">1</span>, <span class="at">data =</span> trial)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1"></a>strial <span class="ot">&lt;-</span> <span class="fu">svydesign</span>(<span class="sc">~</span> <span class="dv">1</span>, <span class="at">data =</span> trial)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
 <pre><code>Warning in svydesign.default(~1, data = trial): No weights or probabilities
 supplied, assuming equal probability</code></pre>
@@ -4014,50 +4132,50 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </div>
 <p>Pour le calcul d’une courbe de Kaplan-Meier, nous allons utiliser la fonction <code><a href="https://rdrr.io/pkg/survey/man/svykm.html">survey::svykm()</a></code>. Il est préférable de préciser <code>se = TRUE</code> pour que les erreurs standards soient calculées, ce qui permettra plus tard de récupérer les intervalles de confiance.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1"></a>skm <span class="ot">&lt;-</span> <span class="fu">svykm</span>(</span>
-<span id="cb35-2"><a href="#cb35-2"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> <span class="dv">1</span>,</span>
-<span id="cb35-3"><a href="#cb35-3"></a>  <span class="at">design =</span> strial,</span>
-<span id="cb35-4"><a href="#cb35-4"></a>  <span class="at">se =</span> <span class="cn">TRUE</span></span>
-<span id="cb35-5"><a href="#cb35-5"></a>)</span>
-<span id="cb35-6"><a href="#cb35-6"></a>skm_trt <span class="ot">&lt;-</span> <span class="fu">svykm</span>(</span>
-<span id="cb35-7"><a href="#cb35-7"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> trt,</span>
-<span id="cb35-8"><a href="#cb35-8"></a>  <span class="at">design =</span> strial,</span>
-<span id="cb35-9"><a href="#cb35-9"></a>  <span class="at">se =</span> <span class="cn">TRUE</span></span>
-<span id="cb35-10"><a href="#cb35-10"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb45"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1"></a>skm <span class="ot">&lt;-</span> <span class="fu">svykm</span>(</span>
+<span id="cb45-2"><a href="#cb45-2"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> <span class="dv">1</span>,</span>
+<span id="cb45-3"><a href="#cb45-3"></a>  <span class="at">design =</span> strial,</span>
+<span id="cb45-4"><a href="#cb45-4"></a>  <span class="at">se =</span> <span class="cn">TRUE</span></span>
+<span id="cb45-5"><a href="#cb45-5"></a>)</span>
+<span id="cb45-6"><a href="#cb45-6"></a>skm_trt <span class="ot">&lt;-</span> <span class="fu">svykm</span>(</span>
+<span id="cb45-7"><a href="#cb45-7"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> trt,</span>
+<span id="cb45-8"><a href="#cb45-8"></a>  <span class="at">design =</span> strial,</span>
+<span id="cb45-9"><a href="#cb45-9"></a>  <span class="at">se =</span> <span class="cn">TRUE</span></span>
+<span id="cb45-10"><a href="#cb45-10"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Malheureusement, <code><a href="http://www.danieldsjoberg.com/ggsurvfit/reference/ggsurvfit.html">ggsurvfit::ggsurvfit()</a></code> et <code><a href="https://www.danieldsjoberg.com/gtsummary/reference/tbl_survfit.html">gtsummary::tbl_survfit()</a></code> ne sont pas (encore ?) compatibles avec les courbes de survies créées avec <code><a href="https://rdrr.io/pkg/survey/man/svykm.html">survey::svykm()</a></code>. On devra donc avoir recours aux fonctions de bases.</p>
 <p>Pour un graphique avec affichage des intervalles de confiance&nbsp;:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1"></a>skm <span class="sc">|&gt;</span>  <span class="fu">plot</span>(<span class="at">ci =</span> <span class="cn">TRUE</span>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1"></a>skm <span class="sc">|&gt;</span>  <span class="fu">plot</span>(<span class="at">ci =</span> <span class="cn">TRUE</span>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<figure class="figure"><p><a href="analyse-survie_files/figure-html/unnamed-chunk-23-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-12"><img src="analyse-survie_files/figure-html/unnamed-chunk-23-1.png" class="img-fluid figure-img" width="672"></a></p>
+<figure class="figure"><p><a href="analyse-survie_files/figure-html/unnamed-chunk-27-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-12"><img src="analyse-survie_files/figure-html/unnamed-chunk-27-1.png" class="img-fluid figure-img" width="672"></a></p>
 </figure>
 </div>
 </div>
 </div>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1"></a>skm_trt <span class="sc">|&gt;</span> <span class="fu">plot</span>(</span>
-<span id="cb37-2"><a href="#cb37-2"></a>  <span class="at">ci =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb37-3"><a href="#cb37-3"></a>  <span class="at">pars =</span> <span class="fu">list</span>(</span>
-<span id="cb37-4"><a href="#cb37-4"></a>    <span class="at">col =</span> <span class="fu">c</span>(<span class="st">"red"</span>, <span class="st">"blue"</span>)</span>
-<span id="cb37-5"><a href="#cb37-5"></a>  ),</span>
-<span id="cb37-6"><a href="#cb37-6"></a>  <span class="at">lwd =</span> <span class="dv">2</span></span>
-<span id="cb37-7"><a href="#cb37-7"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb47"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1"></a>skm_trt <span class="sc">|&gt;</span> <span class="fu">plot</span>(</span>
+<span id="cb47-2"><a href="#cb47-2"></a>  <span class="at">ci =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb47-3"><a href="#cb47-3"></a>  <span class="at">pars =</span> <span class="fu">list</span>(</span>
+<span id="cb47-4"><a href="#cb47-4"></a>    <span class="at">col =</span> <span class="fu">c</span>(<span class="st">"red"</span>, <span class="st">"blue"</span>)</span>
+<span id="cb47-5"><a href="#cb47-5"></a>  ),</span>
+<span id="cb47-6"><a href="#cb47-6"></a>  <span class="at">lwd =</span> <span class="dv">2</span></span>
+<span id="cb47-7"><a href="#cb47-7"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 <div>
-<figure class="figure"><p><a href="analyse-survie_files/figure-html/unnamed-chunk-24-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-13"><img src="analyse-survie_files/figure-html/unnamed-chunk-24-1.png" class="img-fluid figure-img" width="672"></a></p>
+<figure class="figure"><p><a href="analyse-survie_files/figure-html/unnamed-chunk-28-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-13"><img src="analyse-survie_files/figure-html/unnamed-chunk-28-1.png" class="img-fluid figure-img" width="672"></a></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Pour récupérer la durée à laquelle certains quantiles son atteints&nbsp;:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1"></a>skm <span class="sc">|&gt;</span></span>
-<span id="cb38-2"><a href="#cb38-2"></a>  <span class="fu">quantile</span>(</span>
-<span id="cb38-3"><a href="#cb38-3"></a>    <span class="at">probs =</span> <span class="fu">c</span>(<span class="dv">1</span>, .<span class="dv">75</span>, .<span class="dv">5</span>),</span>
-<span id="cb38-4"><a href="#cb38-4"></a>    <span class="at">ci =</span> <span class="cn">TRUE</span></span>
-<span id="cb38-5"><a href="#cb38-5"></a>  )</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb48"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1"></a>skm <span class="sc">|&gt;</span></span>
+<span id="cb48-2"><a href="#cb48-2"></a>  <span class="fu">quantile</span>(</span>
+<span id="cb48-3"><a href="#cb48-3"></a>    <span class="at">probs =</span> <span class="fu">c</span>(<span class="dv">1</span>, .<span class="dv">75</span>, .<span class="dv">5</span>),</span>
+<span id="cb48-4"><a href="#cb48-4"></a>    <span class="at">ci =</span> <span class="cn">TRUE</span></span>
+<span id="cb48-5"><a href="#cb48-5"></a>  )</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>    1  0.75   0.5 
  3.53 16.07 22.41 
@@ -4070,22 +4188,22 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </div>
 <p>On ne peut pas facilement accéder aux valeurs de la courbe de survie à certains points de temps. Mais la fonction suivante permets de la faire aisément. Il faut lui passer, via <code>times</code>, une liste de points de temps.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1"></a>svykm_time_values <span class="ot">&lt;-</span> <span class="cf">function</span>(x, times, <span class="at">ci_level =</span> .<span class="dv">95</span>) {</span>
-<span id="cb40-2"><a href="#cb40-2"></a>  idx <span class="ot">&lt;-</span> <span class="fu">sapply</span>(</span>
-<span id="cb40-3"><a href="#cb40-3"></a>    times,</span>
-<span id="cb40-4"><a href="#cb40-4"></a>    <span class="cf">function</span>(t) <span class="fu">max</span>(<span class="fu">which</span>(x<span class="sc">$</span>time <span class="sc">&lt;=</span> t))</span>
-<span id="cb40-5"><a href="#cb40-5"></a>  )</span>
-<span id="cb40-6"><a href="#cb40-6"></a>  ci <span class="ot">&lt;-</span> <span class="fu">confint</span>(x, <span class="at">parm =</span> times, <span class="at">level =</span> ci_level)</span>
-<span id="cb40-7"><a href="#cb40-7"></a>  dplyr<span class="sc">::</span><span class="fu">tibble</span>(</span>
-<span id="cb40-8"><a href="#cb40-8"></a>    <span class="at">time =</span> times,</span>
-<span id="cb40-9"><a href="#cb40-9"></a>    <span class="at">estimate =</span> x<span class="sc">$</span>surv[idx],</span>
-<span id="cb40-10"><a href="#cb40-10"></a>    <span class="at">conf.low =</span> ci[, <span class="dv">1</span>],</span>
-<span id="cb40-11"><a href="#cb40-11"></a>    <span class="at">conf.high =</span> ci[, <span class="dv">2</span>]</span>
-<span id="cb40-12"><a href="#cb40-12"></a>  )</span>
-<span id="cb40-13"><a href="#cb40-13"></a>}</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb50"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1"></a>svykm_time_values <span class="ot">&lt;-</span> <span class="cf">function</span>(x, times, <span class="at">ci_level =</span> .<span class="dv">95</span>) {</span>
+<span id="cb50-2"><a href="#cb50-2"></a>  idx <span class="ot">&lt;-</span> <span class="fu">sapply</span>(</span>
+<span id="cb50-3"><a href="#cb50-3"></a>    times,</span>
+<span id="cb50-4"><a href="#cb50-4"></a>    <span class="cf">function</span>(t) <span class="fu">max</span>(<span class="fu">which</span>(x<span class="sc">$</span>time <span class="sc">&lt;=</span> t))</span>
+<span id="cb50-5"><a href="#cb50-5"></a>  )</span>
+<span id="cb50-6"><a href="#cb50-6"></a>  ci <span class="ot">&lt;-</span> <span class="fu">confint</span>(x, <span class="at">parm =</span> times, <span class="at">level =</span> ci_level)</span>
+<span id="cb50-7"><a href="#cb50-7"></a>  dplyr<span class="sc">::</span><span class="fu">tibble</span>(</span>
+<span id="cb50-8"><a href="#cb50-8"></a>    <span class="at">time =</span> times,</span>
+<span id="cb50-9"><a href="#cb50-9"></a>    <span class="at">estimate =</span> x<span class="sc">$</span>surv[idx],</span>
+<span id="cb50-10"><a href="#cb50-10"></a>    <span class="at">conf.low =</span> ci[, <span class="dv">1</span>],</span>
+<span id="cb50-11"><a href="#cb50-11"></a>    <span class="at">conf.high =</span> ci[, <span class="dv">2</span>]</span>
+<span id="cb50-12"><a href="#cb50-12"></a>  )</span>
+<span id="cb50-13"><a href="#cb50-13"></a>}</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1"></a>skm <span class="sc">|&gt;</span> <span class="fu">svykm_time_values</span>(<span class="fu">c</span>(<span class="dv">5</span>, <span class="dv">10</span>, <span class="dv">15</span>, <span class="dv">20</span>))</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb51"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1"></a>skm <span class="sc">|&gt;</span> <span class="fu">svykm_time_values</span>(<span class="fu">c</span>(<span class="dv">5</span>, <span class="dv">10</span>, <span class="dv">15</span>, <span class="dv">20</span>))</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 4 × 4
    time estimate conf.low conf.high
@@ -4098,39 +4216,39 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </div>
 <p>Pour le modèle de Cox, on aura recours à la fonction <code><a href="https://rdrr.io/pkg/survey/man/svycoxph.html">survey::svycoxph()</a></code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1"></a>mods <span class="ot">&lt;-</span> <span class="fu">svycoxph</span>(</span>
-<span id="cb43-2"><a href="#cb43-2"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> stage <span class="sc">+</span> trt <span class="sc">+</span> age,</span>
-<span id="cb43-3"><a href="#cb43-3"></a>  <span class="at">design =</span> strial</span>
-<span id="cb43-4"><a href="#cb43-4"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1"></a>mods <span class="ot">&lt;-</span> <span class="fu">svycoxph</span>(</span>
+<span id="cb53-2"><a href="#cb53-2"></a>  <span class="fu">Surv</span>(ttdeath, death) <span class="sc">~</span> stage <span class="sc">+</span> trt <span class="sc">+</span> age,</span>
+<span id="cb53-3"><a href="#cb53-3"></a>  <span class="at">design =</span> strial</span>
+<span id="cb53-4"><a href="#cb53-4"></a>)</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Cette fois-ci, il est possible d’utiliser <code><a href="https://www.danieldsjoberg.com/gtsummary/reference/tbl_regression.html">gtsummary::tbl_regression()</a></code> ou <code><a href="https://larmarange.github.io/ggstats/reference/ggcoef_model.html">ggstats::ggcoef_model()</a></code> sans problème.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1"></a>mods <span class="sc">|&gt;</span> </span>
-<span id="cb44-2"><a href="#cb44-2"></a>  <span class="fu">tbl_regression</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span> </span>
-<span id="cb44-3"><a href="#cb44-3"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb54"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1"></a>mods <span class="sc">|&gt;</span> </span>
+<span id="cb54-2"><a href="#cb54-2"></a>  <span class="fu">tbl_regression</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb54-3"><a href="#cb54-3"></a>  <span class="fu">bold_labels</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Independent Sampling design (with replacement)
 svydesign(~1, data = trial)</code></pre>
 </div>
 <div class="cell-output-display">
 <div>
-<div id="ehklvlfltp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
-<style>#ehklvlfltp table {
+<div id="qysodqkwsu" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+<style>#qysodqkwsu table {
   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
   -webkit-font-smoothing: antialiased;
   -moz-osx-font-smoothing: grayscale;
 }
 
-#ehklvlfltp thead, #ehklvlfltp tbody, #ehklvlfltp tfoot, #ehklvlfltp tr, #ehklvlfltp td, #ehklvlfltp th {
+#qysodqkwsu thead, #qysodqkwsu tbody, #qysodqkwsu tfoot, #qysodqkwsu tr, #qysodqkwsu td, #qysodqkwsu th {
   border-style: none;
 }
 
-#ehklvlfltp p {
+#qysodqkwsu p {
   margin: 0;
   padding: 0;
 }
 
-#ehklvlfltp .gt_table {
+#qysodqkwsu .gt_table {
   display: table;
   border-collapse: collapse;
   line-height: normal;
@@ -4156,12 +4274,12 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-left-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_caption {
+#qysodqkwsu .gt_caption {
   padding-top: 4px;
   padding-bottom: 4px;
 }
 
-#ehklvlfltp .gt_title {
+#qysodqkwsu .gt_title {
   color: #333333;
   font-size: 125%;
   font-weight: initial;
@@ -4173,7 +4291,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-width: 0;
 }
 
-#ehklvlfltp .gt_subtitle {
+#qysodqkwsu .gt_subtitle {
   color: #333333;
   font-size: 85%;
   font-weight: initial;
@@ -4185,7 +4303,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-width: 0;
 }
 
-#ehklvlfltp .gt_heading {
+#qysodqkwsu .gt_heading {
   background-color: #FFFFFF;
   text-align: center;
   border-bottom-color: #FFFFFF;
@@ -4197,13 +4315,13 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_bottom_border {
+#qysodqkwsu .gt_bottom_border {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_col_headings {
+#qysodqkwsu .gt_col_headings {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -4218,7 +4336,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_col_heading {
+#qysodqkwsu .gt_col_heading {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -4238,7 +4356,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#ehklvlfltp .gt_column_spanner_outer {
+#qysodqkwsu .gt_column_spanner_outer {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -4250,15 +4368,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 4px;
 }
 
-#ehklvlfltp .gt_column_spanner_outer:first-child {
+#qysodqkwsu .gt_column_spanner_outer:first-child {
   padding-left: 0;
 }
 
-#ehklvlfltp .gt_column_spanner_outer:last-child {
+#qysodqkwsu .gt_column_spanner_outer:last-child {
   padding-right: 0;
 }
 
-#ehklvlfltp .gt_column_spanner {
+#qysodqkwsu .gt_column_spanner {
   border-bottom-style: solid;
   border-bottom-width: 2px;
   border-bottom-color: #D3D3D3;
@@ -4270,11 +4388,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   width: 100%;
 }
 
-#ehklvlfltp .gt_spanner_row {
+#qysodqkwsu .gt_spanner_row {
   border-bottom-style: hidden;
 }
 
-#ehklvlfltp .gt_group_heading {
+#qysodqkwsu .gt_group_heading {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -4300,7 +4418,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   text-align: left;
 }
 
-#ehklvlfltp .gt_empty_group_heading {
+#qysodqkwsu .gt_empty_group_heading {
   padding: 0.5px;
   color: #333333;
   background-color: #FFFFFF;
@@ -4315,15 +4433,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: middle;
 }
 
-#ehklvlfltp .gt_from_md > :first-child {
+#qysodqkwsu .gt_from_md > :first-child {
   margin-top: 0;
 }
 
-#ehklvlfltp .gt_from_md > :last-child {
+#qysodqkwsu .gt_from_md > :last-child {
   margin-bottom: 0;
 }
 
-#ehklvlfltp .gt_row {
+#qysodqkwsu .gt_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -4342,7 +4460,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   overflow-x: hidden;
 }
 
-#ehklvlfltp .gt_stub {
+#qysodqkwsu .gt_stub {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -4355,7 +4473,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ehklvlfltp .gt_stub_row_group {
+#qysodqkwsu .gt_stub_row_group {
   color: #333333;
   background-color: #FFFFFF;
   font-size: 100%;
@@ -4369,15 +4487,15 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   vertical-align: top;
 }
 
-#ehklvlfltp .gt_row_group_first td {
+#qysodqkwsu .gt_row_group_first td {
   border-top-width: 2px;
 }
 
-#ehklvlfltp .gt_row_group_first th {
+#qysodqkwsu .gt_row_group_first th {
   border-top-width: 2px;
 }
 
-#ehklvlfltp .gt_summary_row {
+#qysodqkwsu .gt_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -4387,16 +4505,16 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ehklvlfltp .gt_first_summary_row {
+#qysodqkwsu .gt_first_summary_row {
   border-top-style: solid;
   border-top-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_first_summary_row.thick {
+#qysodqkwsu .gt_first_summary_row.thick {
   border-top-width: 2px;
 }
 
-#ehklvlfltp .gt_last_summary_row {
+#qysodqkwsu .gt_last_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -4406,7 +4524,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_grand_summary_row {
+#qysodqkwsu .gt_grand_summary_row {
   color: #333333;
   background-color: #FFFFFF;
   text-transform: inherit;
@@ -4416,7 +4534,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ehklvlfltp .gt_first_grand_summary_row {
+#qysodqkwsu .gt_first_grand_summary_row {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -4426,7 +4544,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-top-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_last_grand_summary_row_top {
+#qysodqkwsu .gt_last_grand_summary_row_top {
   padding-top: 8px;
   padding-bottom: 8px;
   padding-left: 5px;
@@ -4436,11 +4554,11 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_striped {
+#qysodqkwsu .gt_striped {
   background-color: rgba(128, 128, 128, 0.05);
 }
 
-#ehklvlfltp .gt_table_body {
+#qysodqkwsu .gt_table_body {
   border-top-style: solid;
   border-top-width: 2px;
   border-top-color: #D3D3D3;
@@ -4449,7 +4567,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-bottom-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_footnotes {
+#qysodqkwsu .gt_footnotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -4463,7 +4581,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_footnote {
+#qysodqkwsu .gt_footnote {
   margin: 0px;
   font-size: 90%;
   padding-top: 4px;
@@ -4472,7 +4590,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ehklvlfltp .gt_sourcenotes {
+#qysodqkwsu .gt_sourcenotes {
   color: #333333;
   background-color: #FFFFFF;
   border-bottom-style: none;
@@ -4486,7 +4604,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   border-right-color: #D3D3D3;
 }
 
-#ehklvlfltp .gt_sourcenote {
+#qysodqkwsu .gt_sourcenote {
   font-size: 90%;
   padding-top: 4px;
   padding-bottom: 4px;
@@ -4494,72 +4612,72 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
   padding-right: 5px;
 }
 
-#ehklvlfltp .gt_left {
+#qysodqkwsu .gt_left {
   text-align: left;
 }
 
-#ehklvlfltp .gt_center {
+#qysodqkwsu .gt_center {
   text-align: center;
 }
 
-#ehklvlfltp .gt_right {
+#qysodqkwsu .gt_right {
   text-align: right;
   font-variant-numeric: tabular-nums;
 }
 
-#ehklvlfltp .gt_font_normal {
+#qysodqkwsu .gt_font_normal {
   font-weight: normal;
 }
 
-#ehklvlfltp .gt_font_bold {
+#qysodqkwsu .gt_font_bold {
   font-weight: bold;
 }
 
-#ehklvlfltp .gt_font_italic {
+#qysodqkwsu .gt_font_italic {
   font-style: italic;
 }
 
-#ehklvlfltp .gt_super {
+#qysodqkwsu .gt_super {
   font-size: 65%;
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-#ehklvlfltp div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
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@@ -4655,7 +4773,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </div>
 <p>Enfin, pour le test de l’hypothèse de proportionnalité des risques, on peut utiliser comme précédemment la fonction <code>cox.zph()</code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1"></a>mods <span class="sc">|&gt;</span> <span class="fu">cox.zph</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb56"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1"></a>mods <span class="sc">|&gt;</span> <span class="fu">cox.zph</span>()</span></code><button title="Copier vers le presse-papier" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>          chisq df    p
 stage  0.004915  3 1.00
@@ -5286,7 +5404,7 @@ <h1 class="title"><span id="sec-analyse-survie" class="quarto-section-identifier
 </ul>
 </div>
   </div>
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/analyses_avancees/classification-ascendante-hierarchique.html b/docs/analyses_avancees/classification-ascendante-hierarchique.html
index d62e76b..af7c12b 100644
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
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diff --git a/docs/analyses_avancees/modeles-comptage.html b/docs/analyses_avancees/modeles-comptage.html
index 259a987..3e3cbb4 100644
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diff --git a/docs/analyses_avancees/modeles-incidence.html b/docs/analyses_avancees/modeles-incidence.html
index 2c7ec21..8f55ef8 100644
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;46.1: Facteurs associés à la mortalité des patients atteints d’un cancer (modèle de Poisson)
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 Figure&nbsp;46.2: Facteurs associés au taux de réclamation (modèle de Poisson)
@@ -1976,7 +1976,7 @@ <h1 class="title"><span id="sec-modeles-incidence" class="quarto-section-identif
 <div class="cell-output-display page-columns page-full">
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+<a href="modeles-incidence_files/figure-html/fig-ggcoef_table-mod3_nb-incidence-1.png" class="lightbox" data-gallery="quarto-lightbox-gallery-3" data-glightbox="description: .lightbox-desc-3"><img src="modeles-incidence_files/figure-html/fig-ggcoef_table-mod3_nb-incidence-1.png" class="img-fluid figure-img" width="672"></a>
 </div>
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 Figure&nbsp;46.3: Facteurs associés au taux de fécondité entre 15 et 25 ans (modèle négatif binomial)
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 </ul>
 </div>
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diff --git a/docs/analyses_avancees/modeles-zero-inflated.html b/docs/analyses_avancees/modeles-zero-inflated.html
index 565bf20..c60f0bb 100644
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
   </div>
 </li>
           <li class="sidebar-item">
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;47.1: Comparaison des valeurs observées et des valeurs théoriques
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;47.2: Coefficients du modèle de Poisson <em>zero-inflated</em>
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;47.3: Coefficients du modèle de Poisson <em>zero-inflated</em> simple
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 <div class="cell-output-display page-columns page-full">
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 Figure&nbsp;47.4: Coefficients du modèle négatif binomial <em>hurdle</em>
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 </ul>
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index f326abf..beed39f 100644
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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 Figure&nbsp;44.1: Graphique des coefficients du modèle ordinal
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-      <p class="date">26 septembre 2024</p>
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation/etiquettes-valeurs.html b/docs/manipulation/etiquettes-valeurs.html
index f6db063..caa3a88 100644
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@@ -333,7 +333,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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@@ -789,7 +789,7 @@ <h1 class="title"><span id="sec-etiquettes-valeurs" class="quarto-section-identi
 <p>La question qui peut se poser est donc de choisir à quel moment cette conversion doit avoir lieu dans un processus d’analyse. On peut considérer deux approches principales.</p>
 <div id="fig-conversion-labelled" class="quarto-figure quarto-figure-center quarto-float anchored page-columns page-full">
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+<a href="ressources/conversion_labelled.png" class="lightbox" data-glightbox="description: .lightbox-desc-1" data-gallery="quarto-lightbox-gallery-1"><img src="ressources/conversion_labelled.png" class="img-fluid figure-img"></a>
 </div>
 <figcaption class="quarto-float-caption-margin quarto-float-caption quarto-float-fig margin-caption" id="fig-conversion-labelled-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;12.1: Deux approches possibles pour la conversion des étiquettes de valeurs
@@ -1665,7 +1665,7 @@ <h1 class="title"><span id="sec-etiquettes-valeurs" class="quarto-section-identi
 </ul>
 </div>
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diff --git a/docs/manipulation/etiquettes-variables.html b/docs/manipulation/etiquettes-variables.html
index bc18aaa..318f16f 100644
--- a/docs/manipulation/etiquettes-variables.html
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@@ -333,7 +333,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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@@ -1401,7 +1401,7 @@ <h1 class="title"><span id="sec-etiquettes-variables" class="quarto-section-iden
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diff --git a/docs/manipulation/facteurs.html b/docs/manipulation/facteurs.html
index 4e1a609..d378816 100644
--- a/docs/manipulation/facteurs.html
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@@ -333,7 +333,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
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@@ -1766,7 +1766,7 @@ <h1 class="title"><span id="sec-facteurs" class="quarto-section-identifier"><spa
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index e5e1c29..0b1a304 100644
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@@ -330,7 +330,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation/import-export.html b/docs/manipulation/import-export.html
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+</footer><script>var lightboxQuarto = GLightbox({"descPosition":"bottom","loop":false,"closeEffect":"zoom","openEffect":"zoom","selector":".lightbox"});
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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@@ -1337,7 +1337,7 @@ <h1 class="title"><span id="sec-pipe" class="quarto-section-identifier"><span cl
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index a7828a7..0a5d825 100644
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@@ -330,7 +330,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation_avancee/conditions-logiques.html b/docs/manipulation_avancee/conditions-logiques.html
index fa770bd..3e7f283 100644
--- a/docs/manipulation_avancee/conditions-logiques.html
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@@ -333,7 +333,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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@@ -786,7 +786,7 @@ <h1 class="title"><span id="sec-conditions-logiques" class="quarto-section-ident
 <li><p><code>!</code> : opérateur <q>non</q> (<code>!x</code> est vrai si <code>x</code> est faux).</p></li>
 </ul>
 <div class="quarto-figure quarto-figure-center page-columns page-full">
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+<figure class="figure page-columns page-full"><p><a href="ressources/operations_logiques.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" data-glightbox="description: .lightbox-desc-1" title="Représentation graphique de l’ensemble des opérations logiques. Le cercle de gauche représente x et celui de droite y. La région colorée représente le résultat de l’opération."><img src="ressources/operations_logiques.png" class="img-fluid figure-img" alt="Représentation graphique de l’ensemble des opérations logiques. Le cercle de gauche représente x et celui de droite y. La région colorée représente le résultat de l’opération."></a></p>
 <figcaption class="margin-caption">Représentation graphique de l’ensemble des opérations logiques. Le cercle de gauche représente x et celui de droite y. La région colorée représente le résultat de l’opération.</figcaption></figure>
 </div>
 <p>Ils permettent de combiner plusieurs conditions entre elles.</p>
@@ -1517,7 +1517,7 @@ <h1 class="title"><span id="sec-conditions-logiques" class="quarto-section-ident
 </ul>
 </div>
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-</footer><script>var lightboxQuarto = GLightbox({"selector":".lightbox","closeEffect":"zoom","descPosition":"bottom","openEffect":"zoom","loop":false});
+</footer><script>var lightboxQuarto = GLightbox({"descPosition":"bottom","openEffect":"zoom","closeEffect":"zoom","selector":".lightbox","loop":false});
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diff --git a/docs/manipulation_avancee/dates.html b/docs/manipulation_avancee/dates.html
index 3065652..f156e7a 100644
--- a/docs/manipulation_avancee/dates.html
+++ b/docs/manipulation_avancee/dates.html
@@ -330,7 +330,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation_avancee/fonctions-a-fenetre.html b/docs/manipulation_avancee/fonctions-a-fenetre.html
index 9fadbad..612bfb4 100644
--- a/docs/manipulation_avancee/fonctions-a-fenetre.html
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@@ -330,7 +330,7 @@
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation_avancee/fusion-tables.html b/docs/manipulation_avancee/fusion-tables.html
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--- a/docs/manipulation_avancee/fusion-tables.html
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation_avancee/stringr.html b/docs/manipulation_avancee/stringr.html
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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diff --git a/docs/manipulation_avancee/tidyr.html b/docs/manipulation_avancee/tidyr.html
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- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
+ <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle réduit</span></span></a>
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@@ -2476,7 +2476,7 @@ <h1 class="title"><span id="sec-tidyr" class="quarto-section-identifier"><span c
 </ul>
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+</footer><script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","openEffect":"zoom","loop":false,"selector":".lightbox","descPosition":"bottom"});
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diff --git a/docs/manipulation_avancee/transformations-multiples.html b/docs/manipulation_avancee/transformations-multiples.html
index 1c7743c..dcbeb9d 100644
--- a/docs/manipulation_avancee/transformations-multiples.html
+++ b/docs/manipulation_avancee/transformations-multiples.html
@@ -330,7 +330,7 @@
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../analyses/selection-modele-pas-a-pas.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">23</span>&nbsp; <span class="chapter-title">Sélection pas à pas d’un modèle</span></span></a>
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diff --git a/docs/search.json b/docs/search.json
index e15a012..2643d57 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1329,89 +1329,89 @@
   {
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     "href": "analyses/selection-modele-pas-a-pas.html",
-    "title": "23  Sélection pas à pas d’un modèle",
+    "title": "23  Sélection pas à pas d’un modèle réduit",
     "section": "",
     "text": "23.1 Données d’illustration\nPour illustrer ce chapitre, nous allons prendre un modèle logistique inspiré de celui utilisé dans le chapitre sur la régression logistique binaire (cf. Chapitre 22).\nlibrary(tidyverse)\nlibrary(labelled)\nlibrary(gtsummary)\ntheme_gtsummary_language(\n  \"fr\",\n  decimal.mark = \",\",\n  big.mark = \" \"\n)\n\ndata(hdv2003, package = \"questionr\")\n\nd &lt;-\n  hdv2003 |&gt; \n  mutate(\n    sexe = sexe |&gt; fct_relevel(\"Femme\"),\n    groupe_ages = age |&gt;\n      cut(\n        c(18, 25, 45, 65, 99),\n        right = FALSE,\n        include.lowest = TRUE,\n        labels = c(\"18-24 ans\", \"25-44 ans\",\n                   \"45-64 ans\", \"65 ans et plus\")\n      ),\n    etudes = nivetud |&gt; \n      fct_recode(\n        \"Primaire\" = \"N'a jamais fait d'etudes\",\n        \"Primaire\" = \"A arrete ses etudes, avant la derniere annee d'etudes primaires\",\n        \"Primaire\" = \"Derniere annee d'etudes primaires\",\n        \"Secondaire\" = \"1er cycle\",\n        \"Secondaire\" = \"2eme cycle\",\n        \"Technique / Professionnel\" = \"Enseignement technique ou professionnel court\",\n        \"Technique / Professionnel\" = \"Enseignement technique ou professionnel long\",\n        \"Supérieur\" = \"Enseignement superieur y compris technique superieur\"\n    ) |&gt; \n    fct_na_value_to_level(\"Non documenté\")  \n  ) |&gt; \n  set_variable_labels(\n    sport = \"Pratique un sport ?\",\n    sexe = \"Sexe\",\n    groupe_ages = \"Groupe d'âges\",\n    etudes = \"Niveau d'études\",\n    relig = \"Rapport à la religion\",\n    lecture.bd = \"Lit des bandes dessinées ?\"\n  )\n\nmod &lt;- glm(\n  sport ~ sexe + groupe_ages + etudes + relig + lecture.bd,\n  family = binomial,\n  data = d\n)",
     "crumbs": [
       "**Analyses**",
-      "<span class='chapter-number'>23</span>  <span class='chapter-title'>Sélection pas à pas d'un modèle</span>"
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+    "title": "23  Sélection pas à pas d’un modèle réduit",
     "section": "\n23.2 Présentation de l’AIC",
     "text": "23.2 Présentation de l’AIC\nIl faut définir un critère pour déterminer la qualité d’un modèle. L’un des plus utilisés est le Akaike Information Criterion ou AIC. Il s’agit d’un compromis entre le nombre de degrés de liberté (e.g. le nombre de coefficients dans le modèle) que l’on cherche à minimiser et la variance expliquée que l’on cherche à maximiser (la vraisemblance).\nPlus précisément \\(AIC=2k-2ln(L)\\) où \\(L\\) est le maximum de la fonction de vraisemblance du modèle et \\(k\\) le nombre de paramètres (i.e. de coefficients) du modèle. Plus l’AIC sera faible, meilleur sera le modèle.\nL’AIC d’un modèle s’obtient aisément avec AIC().\n\nAIC(mod)\n\n[1] 2257.101",
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       "**Analyses**",
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     "section": "\n23.3 Sélection pas à pas descendante",
     "text": "23.3 Sélection pas à pas descendante\nLa fonction step() permet de sélectionner le meilleur modèle par une procédure pas à pas descendante basée sur la minimisation de l’AIC. La fonction affiche à l’écran les différentes étapes de la sélection et renvoie le modèle final.\n\nmod2 &lt;- step(mod)\n\nStart:  AIC=2257.1\nsport ~ sexe + groupe_ages + etudes + relig + lecture.bd\n\n              Df Deviance    AIC\n- relig        5   2231.9 2251.9\n- lecture.bd   1   2227.9 2255.9\n&lt;none&gt;             2227.1 2257.1\n- sexe         1   2245.6 2273.6\n- groupe_ages  3   2280.1 2304.1\n- etudes       4   2375.5 2397.5\n\nStep:  AIC=2251.95\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n- lecture.bd   1   2232.6 2250.6\n&lt;none&gt;             2231.9 2251.9\n- sexe         1   2248.8 2266.8\n- groupe_ages  3   2282.1 2296.1\n- etudes       4   2380.5 2392.5\n\nStep:  AIC=2250.56\nsport ~ sexe + groupe_ages + etudes\n\n              Df Deviance    AIC\n&lt;none&gt;             2232.6 2250.6\n- sexe         1   2249.2 2265.2\n- groupe_ages  3   2282.5 2294.5\n- etudes       4   2385.2 2395.2\n\n\nLe modèle initial a un AIC de 2257,1.\nÀ la première étape, il apparaît que la suppression de la variable relig permettrait diminuer l’AIC à 2251,9 et la suppression de la variable lecture.bd de le diminuer à 2255,9. Le gain maximal est obtenu en supprimant relig et donc cette variable est supprimée à ce stade. On peut noter que la suppression de la variable entraîne de facto une augmentation des résidus (colonne Deviance) et donc une baisse de la vraisemblance du modèle, mais cela est compensé par la réduction du nombre de degrés de liberté.\nLe processus est maintenant répété. À la seconde étape, supprimer lecture.bd permettrait de diminuer encore l’AIC à 2250,6 et cette variable est supprimée.\nÀ la troisième étape, tout retrait d’une variable additionnelle reviendrait à augmenter l’AIC.\nLors de la seconde étape, toute suppression d’une autre variable ferait augmenter l’AIC. La procédure s’arrête donc.\nL’objet mod2 renvoyé par step() est le modèle final.\n\nmod2\n\n\nCall:  glm(formula = sport ~ sexe + groupe_ages + etudes, family = binomial, \n    data = d)\n\nCoefficients:\n                    (Intercept)                        sexeHomme  \n                        -1.2815                           0.4234  \n           groupe_ages25-44 ans             groupe_ages45-64 ans  \n                        -0.3012                          -0.9261  \n      groupe_ages65 ans et plus                 etudesSecondaire  \n                        -1.2696                           0.9670  \netudesTechnique / Professionnel                  etudesSupérieur  \n                         1.0678                           1.9955  \n            etudesNon documenté  \n                         2.3192  \n\nDegrees of Freedom: 1999 Total (i.e. Null);  1991 Residual\nNull Deviance:      2617 \nResidual Deviance: 2233     AIC: 2251\n\n\nOn peut effectuer une analyse de variance ou ANOVA pour comparer les deux modèles avec la fonction anova().\n\nanova(mod, mod2, test = \"Chisq\")\n\nAnalysis of Deviance Table\n\nModel 1: sport ~ sexe + groupe_ages + etudes + relig + lecture.bd\nModel 2: sport ~ sexe + groupe_ages + etudes\n  Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)\n1      1985     2227.1                     \n2      1991     2232.6 -6  -5.4597   0.4863\n\n\nIl n’y a pas de différences significatives entre nos deux modèles (p=0,55). Autrement dit, notre second modèle explique tout autant de variance que notre premier modèle, tout en étant plus parcimonieux.\n\n\n\n\n\n\nAstuce\n\n\n\nUne alternative à la fonction step() est la fonction MASS::stepAIC() du package MASS qui fonctionne de la même manière. Si cela ne change rien aux régressions logistiques classiques, il arrive que pour certains types de modèle la méthode step() ne soit pas disponible, mais que MASS::stepAIC() puisse être utilisée à la place.\n\nlibrary(MASS)\n\n\nAttachement du package : 'MASS'\n\n\nL'objet suivant est masqué depuis 'package:gtsummary':\n\n    select\n\n\nL'objet suivant est masqué depuis 'package:dplyr':\n\n    select\n\nmod2bis &lt;- stepAIC(mod)\n\nStart:  AIC=2257.1\nsport ~ sexe + groupe_ages + etudes + relig + lecture.bd\n\n              Df Deviance    AIC\n- relig        5   2231.9 2251.9\n- lecture.bd   1   2227.9 2255.9\n&lt;none&gt;             2227.1 2257.1\n- sexe         1   2245.6 2273.6\n- groupe_ages  3   2280.1 2304.1\n- etudes       4   2375.5 2397.5\n\nStep:  AIC=2251.95\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n- lecture.bd   1   2232.6 2250.6\n&lt;none&gt;             2231.9 2251.9\n- sexe         1   2248.8 2266.8\n- groupe_ages  3   2282.1 2296.1\n- etudes       4   2380.5 2392.5\n\nStep:  AIC=2250.56\nsport ~ sexe + groupe_ages + etudes\n\n              Df Deviance    AIC\n&lt;none&gt;             2232.6 2250.6\n- sexe         1   2249.2 2265.2\n- groupe_ages  3   2282.5 2294.5\n- etudes       4   2385.2 2395.2\n\n\n\n\nOn peut facilement comparer visuellement deux modèles avec ggstats::ggcoef_compare() de ggstats.\n\nlibrary(ggstats)\nggcoef_compare(\n  list(\"modèle complet\" = mod, \"modèle réduit\" = mod2), \n  exponentiate = TRUE\n)\n\n\n\n\n\n\nFigure 23.1: Comparaison visuelle des deux modèles (dodge)\n\n\n\n\n\nggcoef_compare(\n  list(\"modèle complet\" = mod, \"modèle réduit\" = mod2), \n  type = \"faceted\",\n  exponentiate = TRUE\n)\n\n\n\n\n\n\nFigure 23.2: Comparaison visuelle des deux modèles (faceted)",
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     "section": "\n23.4 Sélection pas à pas ascendante",
     "text": "23.4 Sélection pas à pas ascendante\nPour une approche ascendante, nous allons partir d’un modèle vide, c’est-à-dire d’un modèle sans variable explicative avec simplement un intercept.\n\nmod_vide &lt;- glm(\n  sport ~ 1,\n  family = binomial,\n  data = d\n)\n\nNous allons ensuite passer ce modèle vide à step() et préciser, via un élément nommé upper dans une liste passée à l’argument scope, la formule du modèle maximum à considérer. Nous précisons direction = \"forward\" pour indiquer que nous souhaitons une procédure ascendante.\n\nmod3 &lt;- step(\n  mod_vide,\n  direction = \"forward\",\n  scope = list(\n    upper = ~ sexe + groupe_ages + etudes + relig + lecture.bd\n  )\n)\n\nStart:  AIC=2619.11\nsport ~ 1\n\n              Df Deviance    AIC\n+ etudes       4   2294.9 2304.9\n+ groupe_ages  3   2405.4 2413.4\n+ sexe         1   2600.2 2604.2\n+ lecture.bd   1   2612.7 2616.7\n&lt;none&gt;             2617.1 2619.1\n+ relig        5   2608.8 2620.8\n\nStep:  AIC=2304.92\nsport ~ etudes\n\n              Df Deviance    AIC\n+ groupe_ages  3   2249.2 2265.2\n+ sexe         1   2282.5 2294.5\n&lt;none&gt;             2294.9 2304.9\n+ lecture.bd   1   2294.7 2306.7\n+ relig        5   2293.0 2313.0\n\nStep:  AIC=2265.17\nsport ~ etudes + groupe_ages\n\n             Df Deviance    AIC\n+ sexe        1   2232.6 2250.6\n&lt;none&gt;            2249.2 2265.2\n+ lecture.bd  1   2248.8 2266.8\n+ relig       5   2246.0 2272.0\n\nStep:  AIC=2250.56\nsport ~ etudes + groupe_ages + sexe\n\n             Df Deviance    AIC\n&lt;none&gt;            2232.6 2250.6\n+ lecture.bd  1   2231.9 2251.9\n+ relig       5   2227.9 2255.9\n\n\nCette fois-ci, à chaque étape, la fonction step() évalue le gain à ajouter chaque variable dans le modèle, ajoute la variable la plus pertinente, pour recommence le processus jusqu’à ce qu’il n’y ait plus de gain à ajouter une variable au modèle. Notons que nous aboutissons ici au même résultat.\n\n\n\n\n\n\nAstuce\n\n\n\nNous aurions pu nous passer de préciser direction = \"forward\". Dans cette situation, step() regarde simultanément les gains à ajouter une variable additionnelle au modèle et à supprimer une variable déjà inclue pour . Lorsque l’on part d’un modèle vide, cela ne change rien au résultat.\n\nmod3 &lt;- step(\n  mod_vide,\n  scope = list(\n    upper = ~ sexe + groupe_ages + etudes + relig + lecture.bd\n  )\n)\n\nStart:  AIC=2619.11\nsport ~ 1\n\n              Df Deviance    AIC\n+ etudes       4   2294.9 2304.9\n+ groupe_ages  3   2405.4 2413.4\n+ sexe         1   2600.2 2604.2\n+ lecture.bd   1   2612.7 2616.7\n&lt;none&gt;             2617.1 2619.1\n+ relig        5   2608.8 2620.8\n\nStep:  AIC=2304.92\nsport ~ etudes\n\n              Df Deviance    AIC\n+ groupe_ages  3   2249.2 2265.2\n+ sexe         1   2282.5 2294.5\n&lt;none&gt;             2294.9 2304.9\n+ lecture.bd   1   2294.7 2306.7\n+ relig        5   2293.0 2313.0\n- etudes       4   2617.1 2619.1\n\nStep:  AIC=2265.17\nsport ~ etudes + groupe_ages\n\n              Df Deviance    AIC\n+ sexe         1   2232.6 2250.6\n&lt;none&gt;             2249.2 2265.2\n+ lecture.bd   1   2248.8 2266.8\n+ relig        5   2246.0 2272.0\n- groupe_ages  3   2294.9 2304.9\n- etudes       4   2405.4 2413.4\n\nStep:  AIC=2250.56\nsport ~ etudes + groupe_ages + sexe\n\n              Df Deviance    AIC\n&lt;none&gt;             2232.6 2250.6\n+ lecture.bd   1   2231.9 2251.9\n+ relig        5   2227.9 2255.9\n- sexe         1   2249.2 2265.2\n- groupe_ages  3   2282.5 2294.5\n- etudes       4   2385.2 2395.2",
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     "section": "\n23.5 Forcer certaines variables dans le modèle réduit",
     "text": "23.5 Forcer certaines variables dans le modèle réduit\nMême si l’on a recourt à step(), on peut vouloir forcer la présence de certaines variables dans le modèle, même si leur suppression minimiserait l’AIC. Par exemple, si l’on a des hypothèses spécifiques pour ces variables et que l’on a intérêt à montrer qu’elles n’ont pas d’effet dans le modèle multivariable.\nSupposons que nous avons une hypothèse sur le lien entre la pratique d’un sport et la lecture de bandes dessinées. Nous souhaitons donc forcer la présence de la variable lecture.bd dans le modèle final. Cette fois-ci, nous allons indiquer, via la liste passée à scope, un élément lower indiquant le modèle minimum souhaité. Toutes les variables de ce modèle minimum seront donc conserver dans le modèle final.\n\nmod4 &lt;- step(\n  mod,\n  scope = list(\n    lower = ~ lecture.bd\n  )\n)\n\nStart:  AIC=2257.1\nsport ~ sexe + groupe_ages + etudes + relig + lecture.bd\n\n              Df Deviance    AIC\n- relig        5   2231.9 2251.9\n&lt;none&gt;             2227.1 2257.1\n- sexe         1   2245.6 2273.6\n- groupe_ages  3   2280.1 2304.1\n- etudes       4   2375.5 2397.5\n\nStep:  AIC=2251.95\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n&lt;none&gt;             2231.9 2251.9\n- sexe         1   2248.8 2266.8\n- groupe_ages  3   2282.1 2296.1\n- etudes       4   2380.5 2392.5\n\n\nCette fois-ci, nous constatons que la fonction step() n’a pas considéré la suppression éventuelle de la variable lecture.bd qui est donc conservée.\n\nmod4$formula\n\nsport ~ sexe + groupe_ages + etudes + lecture.bd",
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     "section": "\n23.6 Minimisation du BIC",
     "text": "23.6 Minimisation du BIC\nUn critère similaire à l’AIC est le critère BIC (Bayesian Information Criterion) appelé aussi SBC (Schwarz information criterion).\nSa formule est proche de celle de l’AIC : \\(BIC=ln(n)k-2ln(L)\\) où \\(n\\) correspond au nombre d’observations dans l’échantillon. Par rapport à l’AIC, il pénalise donc plus le nombre de degrés de liberté du modèle.\nPour réaliser une sélection pas à pas par optimisation du BIC, on appellera step() en ajoutant l’argument k = log(n) où n est le nombre d’observations inclues dans le modèle. Par défaut, un modèle est calculé en retirant les observations pour lesquelles des données sont manquantes. Dès lors, pour obtenir le nombre exact d’observations incluses dans le modèle, on peut utiliser la syntaxe mod |&gt; model.matrix() |&gt; nrow(), model.matrix() renvoyant la matrice de données ayant servi au calcul du modèle et nrow() le nombre de lignes.\n\nmod5 &lt;- mod |&gt;\n  step(\n    k = mod |&gt; model.matrix() |&gt; nrow() |&gt; log()\n  )\n\nStart:  AIC=2341.11\nsport ~ sexe + groupe_ages + etudes + relig + lecture.bd\n\n              Df Deviance    AIC\n- relig        5   2231.9 2308.0\n- lecture.bd   1   2227.9 2334.3\n&lt;none&gt;             2227.1 2341.1\n- sexe         1   2245.6 2352.0\n- groupe_ages  3   2280.1 2371.3\n- etudes       4   2375.5 2459.1\n\nStep:  AIC=2307.96\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n- lecture.bd   1   2232.6 2301.0\n&lt;none&gt;             2231.9 2308.0\n- sexe         1   2248.8 2317.2\n- groupe_ages  3   2282.1 2335.3\n- etudes       4   2380.5 2426.1\n\nStep:  AIC=2300.97\nsport ~ sexe + groupe_ages + etudes\n\n              Df Deviance    AIC\n&lt;none&gt;             2232.6 2301.0\n- sexe         1   2249.2 2310.0\n- groupe_ages  3   2282.5 2328.1\n- etudes       4   2385.2 2423.2",
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     "section": "\n23.7 Afficher les indicateurs de performance",
     "text": "23.7 Afficher les indicateurs de performance\nIl existe plusieurs indicateurs de performance ou qualité d’un modèle. Pour les calculer/afficher (dont l’AIC et le BIC), on pourra avoir recours à broom::glance() ou encore à performance::model_performance().\n\nmod |&gt; broom::glance()\n\n# A tibble: 1 × 8\n  null.deviance df.null logLik   AIC   BIC deviance df.residual  nobs\n          &lt;dbl&gt;   &lt;int&gt;  &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;    &lt;dbl&gt;       &lt;int&gt; &lt;int&gt;\n1         2617.    1999 -1114. 2257. 2341.    2227.        1985  2000\n\nmod |&gt; performance::model_performance()\n\n# Indices of model performance\n\nAIC      |     AICc |      BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP\n-----------------------------------------------------------------------------------------------------------\n2257.101 | 2257.343 | 2341.115 |     0.183 | 0.434 | 1.000 |    0.557 |      -Inf |           0.001 | 0.623\n\n\nLe fonction performance::compare_performance() permet de comparer rapidement plusieurs modèles.\n\nperformance::compare_performance(mod, mod2, mod4)\n\n# Comparison of Model Performance Indices\n\nName | Model |  AIC (weights) | AICc (weights) |  BIC (weights) | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP\n--------------------------------------------------------------------------------------------------------------------------------------------\nmod  |   glm | 2257.1 (0.025) | 2257.3 (0.023) | 2341.1 (&lt;.001) |     0.183 | 0.434 | 1.000 |    0.557 |      -Inf |           0.001 | 0.623\nmod2 |   glm | 2250.6 (0.651) | 2250.7 (0.654) | 2301.0 (0.971) |     0.181 | 0.435 | 1.000 |    0.558 |      -Inf |           0.002 | 0.622\nmod4 |   glm | 2252.0 (0.325) | 2252.1 (0.323) | 2308.0 (0.029) |     0.181 | 0.435 | 1.000 |    0.558 |      -Inf |           0.002 | 0.622\n\n\nSi l’on souhaite afficher l’AIC (ainsi que d’autres statistiques globales du modèle) en note du tableau des coefficients, on pourra utiliser gtsummary::add_glance_source_note().\n\nmod2 |&gt; \n  tbl_regression(exponentiate = TRUE) |&gt; \n  bold_labels() |&gt; \n  add_glance_source_note()\n\n\n\n\n\n\n\n\nCaractéristique\n\n      \n\nOR\n\n1\n\n      \n\n95% IC\n\n1\n\n      \np-valeur\n\n    \n\n\nSexe\n\n\n\n\n\n    Femme\n—\n—\n\n\n\n    Homme\n1,53\n1,25 – 1,87\n&lt;0,001\n\n\nGroupe d'âges\n\n\n\n\n\n    18-24 ans\n—\n—\n\n\n\n    25-44 ans\n0,74\n0,48 – 1,15\n0,2\n\n\n    45-64 ans\n0,40\n0,25 – 0,62\n&lt;0,001\n\n\n    65 ans et plus\n0,28\n0,17 – 0,47\n&lt;0,001\n\n\nNiveau d'études\n\n\n\n\n\n    Primaire\n—\n—\n\n\n\n    Secondaire\n2,63\n1,80 – 3,88\n&lt;0,001\n\n\n    Technique / Professionnel\n2,91\n2,03 – 4,22\n&lt;0,001\n\n\n    Supérieur\n7,36\n5,10 – 10,8\n&lt;0,001\n\n\n    Non documenté\n10,2\n5,43 – 19,4\n&lt;0,001\n\n\n\n\ndéviance nulle = 2 617; degrés de liberté du modèle nul = 1 999; Log-likelihood = -1 116; AIC = 2 251; BIC = 2 301; Deviance = 2 233; degrés de liberté des résidus = 1 991; No. Obs. = 2 000\n\n    \n\n\n1 \nOR = rapport de cotes, IC = intervalle de confiance\n\n\n    \n\n\n\n\n\nTable 23.1: Modèle obtenu après réduction du nombre de variables",
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     "section": "\n23.8 Sélection pas à pas et valeurs manquantes",
-    "text": "23.8 Sélection pas à pas et valeurs manquantes\nSi certaines de nos variables explications contiennent des valeurs manquantes (NA), cela peut entraîner des erreurs au moment d’avoir recours à step(), car le nombre d’observations dans le modèle va changer si on retire du modèle une variable explicative avec des valeurs manquantes.\nPrenons un exemple, en ajoutant des valeurs manquantes à la variable relig (pour cela nous allons recoder les refus et les ne sait pas en NA).\n\nd$relig_na &lt;- \n  d$relig |&gt; \n  fct_recode(\n    NULL = \"Rejet\",\n    NULL = \"NSP ou NVPR\"\n  )\n\nmod_na &lt;- glm(\n  sport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd,\n  family = binomial,\n  data = d\n)\n\nAu moment d’exécuter step() nous obtenons l’erreur mentionnée précédemment.\n\nstep(mod_na)\n\nStart:  AIC=2096.64\nsport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd\n\n              Df Deviance    AIC\n- relig_na     3   2073.2 2093.2\n- lecture.bd   1   2072.2 2096.2\n&lt;none&gt;             2070.6 2096.6\n- sexe         1   2088.6 2112.6\n- groupe_ages  3   2118.0 2138.0\n- etudes       4   2218.1 2236.1\n\n\nError in step(mod_na): le nombre de lignes utilisées a changé : supprimer les valeurs manquantes ?\n\n\nPas d’inquiétude ! Il y a moyen de s’en sortir en adoptant la stratégie suivante :\n\ncréer une copie du jeu de données avec uniquement des observations sans valeur manquante pour nos variables explicatives ;\ncalculer notre modèle complet à partir de ce jeu de données ;\nappliquer step() ;\nrecalculer le modèle réduit en repartant du jeu de données complet.\n\nPremière étape, ne garder que les observations complètes à l’aide de tidyr::drop_na(), en lui indiquant la liste des variables dans lesquelles vérifier la présence ou non de NA.\n\nd_complet &lt;- d |&gt; \n  drop_na(sexe, groupe_ages, etudes, relig_na, lecture.bd)\n\nDeuxième étape, calculons le modèle complet avec ce jeu données.\n\nmod_na_alt &lt;- glm(\n  sport ~ sexe + groupe_ages + etudes + relig_na +lecture.bd,\n  family = binomial,\n  data = d_complet\n)\n\nLe modèle mod_na_alt est tout à fait identique au modèle mod_na, car glm() supprime de lui-même les valeurs manquantes quand elles existent. Nous pouvons maintenant utiliser step().\n\nmod_na_reduit &lt;- step(mod_na_alt)\n\nStart:  AIC=2096.64\nsport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd\n\n              Df Deviance    AIC\n- relig_na     3   2073.2 2093.2\n- lecture.bd   1   2072.2 2096.2\n&lt;none&gt;             2070.6 2096.6\n- sexe         1   2088.6 2112.6\n- groupe_ages  3   2118.0 2138.0\n- etudes       4   2218.1 2236.1\n\nStep:  AIC=2093.19\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n- lecture.bd   1   2074.6 2092.6\n&lt;none&gt;             2073.2 2093.2\n- sexe         1   2090.2 2108.2\n- groupe_ages  3   2118.5 2132.5\n- etudes       4   2221.4 2233.4\n\nStep:  AIC=2092.59\nsport ~ sexe + groupe_ages + etudes\n\n              Df Deviance    AIC\n&lt;none&gt;             2074.6 2092.6\n- sexe         1   2091.1 2107.1\n- groupe_ages  3   2119.6 2131.6\n- etudes       4   2227.2 2237.2\n\n\nCela s’exécute sans problème car tous les sous-modèles sont calculés à partir de d_complet et donc ont bien le même nombre d’observations. Cependant, dans notre modèle réduit, on a retiré 137 observations en raison d’une valeur manquante sur la variable relig_na, variable qui n’est plus présente dans notre modèle réduit. Il serait donc pertinent de réintégrer ces observations.\nNous allons donc recalculer le modèle réduit mais à partir de d. Inutile de recopier à la main la formule du modèle réduit, car nous pouvons l’obtenir directement avec mod_na_reduit$formula.\n\nmod_na_reduit2 &lt;- glm(\n  mod_na_reduit$formula,\n  family = binomial,\n  data = d\n)\n\nAttention : mod_na_reduit et mod_na_reduit2 ne sont pas identiques puisque le second a été calculé sur un plus grand nombre d’observations, ce qui change très légèrement les valeurs des coefficients.\n\n\n\n\n\n\nAstuce\n\n\n\nPour automatiser l’ensemble de ce processus, on peut copier/coller le code de la fonction générique suivante1 :\n\nstep_with_na &lt;- function(model,\n                         full_data = NULL, ...) {\n  # assign the model in parent environment\n  assign(\"...full.model\", model, pos = 1) \n  # refit the model without NAs\n  model_no_na &lt;- update(\n    ...full.model,\n    formula = terms(...full.model),\n    data = model.frame(...full.model))\n  # apply step()\n  model_simplified &lt;- step(model_no_na, ...)\n  # recompute simplified model using full data\n  if (is.null(full_data)) {\n    update(model,\n           formula = terms(model_simplified))\n  } else {\n    update(model,\n           formula = terms(model_simplified),\n           data = full_data)\n  }\n}\n\nElle réalise l’ensemble des opérations décrites plus haut en profitant de la flexibilité offerte par la fonction update(). La fonction model.frame() permet de récupérer le jeu de données utilisé par un modèle (et dans lequel les lignes incomplètes ont été supprimées). La fonction terms() permet de récupérer l’équation du modèle.\nAttention : il s’agit d’une fonction expérimentale et elle n’est peut-être pas compatible avec tous les types de modèles. Elle a été testée avec les modèles lm(), glm() et nnet::multinom().\n\nmod_na_reduit_direct &lt;- step_with_na(mod_na, trace = 0)\n\nLe résultat obtenu est strictement identique.\n\nanova(mod_na_reduit2, mod_na_reduit_direct)\n\nAnalysis of Deviance Table\n\nModel 1: sport ~ sexe + groupe_ages + etudes\nModel 2: sport ~ sexe + groupe_ages + etudes\n  Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)\n1      1991     2232.6                     \n2      1991     2232.6  0        0         \n\n\n\n\n\n\n1 L’argument full_data peut-être utile lorsque le jeu de données n’est pas disponible dans l’environnement parent, par exemple lorsque l’on imbrique des calculs avec lapply() ou purrr:map(). On pourra lors passer manuellement le jeu de données complet à la fonction.\nFigure 23.1: Comparaison visuelle des deux modèles (dodge)\nFigure 23.2: Comparaison visuelle des deux modèles (faceted)",
+    "text": "23.8 Sélection pas à pas et valeurs manquantes\nSi certaines de nos variables explications contiennent des valeurs manquantes (NA), cela peut entraîner des erreurs au moment d’avoir recours à step(), car le nombre d’observations dans le modèle va changer si on retire du modèle une variable explicative avec des valeurs manquantes.\nPrenons un exemple, en ajoutant des valeurs manquantes à la variable relig (pour cela nous allons recoder les refus et les ne sait pas en NA).\n\nd$relig_na &lt;- \n  d$relig |&gt; \n  fct_recode(\n    NULL = \"Rejet\",\n    NULL = \"NSP ou NVPR\"\n  )\n\nmod_na &lt;- glm(\n  sport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd,\n  family = binomial,\n  data = d\n)\n\nAu moment d’exécuter step() nous obtenons l’erreur mentionnée précédemment.\n\nstep(mod_na)\n\nStart:  AIC=2096.64\nsport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd\n\n              Df Deviance    AIC\n- relig_na     3   2073.2 2093.2\n- lecture.bd   1   2072.2 2096.2\n&lt;none&gt;             2070.6 2096.6\n- sexe         1   2088.6 2112.6\n- groupe_ages  3   2118.0 2138.0\n- etudes       4   2218.1 2236.1\n\n\nError in step(mod_na): le nombre de lignes utilisées a changé : supprimer les valeurs manquantes ?\n\n\nPas d’inquiétude ! Il y a moyen de s’en sortir en adoptant la stratégie suivante :\n\ncréer une copie du jeu de données avec uniquement des observations sans valeur manquante pour nos variables explicatives ;\ncalculer notre modèle complet à partir de ce jeu de données ;\nappliquer step() ;\nrecalculer le modèle réduit en repartant du jeu de données complet.\n\nPremière étape, ne garder que les observations complètes à l’aide de tidyr::drop_na(), en lui indiquant la liste des variables dans lesquelles vérifier la présence ou non de NA.\n\nd_complet &lt;- d |&gt; \n  drop_na(sexe, groupe_ages, etudes, relig_na, lecture.bd)\n\nDeuxième étape, calculons le modèle complet avec ce jeu données.\n\nmod_na_alt &lt;- glm(\n  sport ~ sexe + groupe_ages + etudes + relig_na +lecture.bd,\n  family = binomial,\n  data = d_complet\n)\n\nLe modèle mod_na_alt est tout à fait identique au modèle mod_na, car glm() supprime de lui-même les valeurs manquantes quand elles existent. Nous pouvons maintenant utiliser step().\n\nmod_na_reduit &lt;- step(mod_na_alt)\n\nStart:  AIC=2096.64\nsport ~ sexe + groupe_ages + etudes + relig_na + lecture.bd\n\n              Df Deviance    AIC\n- relig_na     3   2073.2 2093.2\n- lecture.bd   1   2072.2 2096.2\n&lt;none&gt;             2070.6 2096.6\n- sexe         1   2088.6 2112.6\n- groupe_ages  3   2118.0 2138.0\n- etudes       4   2218.1 2236.1\n\nStep:  AIC=2093.19\nsport ~ sexe + groupe_ages + etudes + lecture.bd\n\n              Df Deviance    AIC\n- lecture.bd   1   2074.6 2092.6\n&lt;none&gt;             2073.2 2093.2\n- sexe         1   2090.2 2108.2\n- groupe_ages  3   2118.5 2132.5\n- etudes       4   2221.4 2233.4\n\nStep:  AIC=2092.59\nsport ~ sexe + groupe_ages + etudes\n\n              Df Deviance    AIC\n&lt;none&gt;             2074.6 2092.6\n- sexe         1   2091.1 2107.1\n- groupe_ages  3   2119.6 2131.6\n- etudes       4   2227.2 2237.2\n\n\nCela s’exécute sans problème car tous les sous-modèles sont calculés à partir de d_complet et donc ont bien le même nombre d’observations. Cependant, dans notre modèle réduit, on a retiré 137 observations en raison d’une valeur manquante sur la variable relig_na, variable qui n’est plus présente dans notre modèle réduit. Il serait donc pertinent de réintégrer ces observations.\nNous allons donc recalculer le modèle réduit mais à partir de d. Inutile de recopier à la main la formule du modèle réduit, car nous pouvons l’obtenir directement avec mod_na_reduit$formula.\n\nmod_na_reduit2 &lt;- glm(\n  mod_na_reduit$formula,\n  family = binomial,\n  data = d\n)\n\nAttention : mod_na_reduit et mod_na_reduit2 ne sont pas identiques puisque le second a été calculé sur un plus grand nombre d’observations, ce qui change très légèrement les valeurs des coefficients.\n\n\n\n\n\n\nAstuce\n\n\n\nPour automatiser l’ensemble de ce processus, on peut copier/coller le code de la fonction générique suivante1 :\n\nstep_with_na &lt;- function(model,\n                         full_data = NULL, ...) {\n  # data with no NAs\n  if (is.null(full_data)) {\n    ...data_no_na &lt;- model.frame(model)\n  } else {\n    ...data_no_na &lt;- get_all_vars(model, data = full_data)\n    ...data_no_na &lt;- ...data_no_na[complete.cases(...data_no_na), ]\n  }\n  \n  # assign ...data_no_na in parent.frame()\n  assign(\"...data_no_na\", ...data_no_na, envir = parent.frame())\n  \n  # refit the model without NAs\n  model_no_na &lt;- update(\n    model,\n    formula = terms(model),\n    data = ...data_no_na\n  )\n  \n  # apply step()\n  model_simplified &lt;- step(model_no_na, ...)\n  \n  # recompute simplified model using full data\n  if (is.null(full_data)) {\n    update(\n      model,\n      formula = terms(model_simplified)\n    )\n  } else {\n    update(\n      model,\n      formula = terms(model_simplified),\n      data = full_data\n    )\n  }\n}\n\nElle réalise l’ensemble des opérations décrites plus haut en profitant de la flexibilité offerte par la fonction update(). La fonction model.frame() permet de récupérer le jeu de données utilisé par un modèle (et dans lequel les lignes incomplètes ont été supprimées). La fonction terms() permet de récupérer l’équation du modèle.\nAttention : il s’agit d’une fonction expérimentale et elle n’est peut-être pas compatible avec tous les types de modèles. Elle a été testée avec les modèles lm(), glm() et nnet::multinom().\n\nmod_na_reduit_direct &lt;- step_with_na(mod_na, trace = 0)\n\nLe résultat obtenu est strictement identique.\n\nanova(mod_na_reduit2, mod_na_reduit_direct)\n\nAnalysis of Deviance Table\n\nModel 1: sport ~ sexe + groupe_ages + etudes\nModel 2: sport ~ sexe + groupe_ages + etudes\n  Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)\n1      1991     2232.6                     \n2      1991     2232.6  0        0         \n\n\n\n\n\n\n1 L’argument full_data peut-être utile lorsque le jeu de données n’est pas disponible dans l’environnement parent, par exemple lorsque l’on imbrique des calculs avec lapply() ou purrr:map(). On pourra lors passer manuellement le jeu de données complet à la fonction.\nFigure 23.1: Comparaison visuelle des deux modèles (dodge)\nFigure 23.2: Comparaison visuelle des deux modèles (faceted)",
     "crumbs": [
       "**Analyses**",
-      "<span class='chapter-number'>23</span>  <span class='chapter-title'>Sélection pas à pas d'un modèle</span>"
+      "<span class='chapter-number'>23</span>  <span class='chapter-title'>Sélection pas à pas d'un modèle réduit</span>"
     ]
   },
   {
@@ -2662,7 +2662,7 @@
     "href": "analyses_avancees/analyse-survie.html#analyse-multivariable-modèle-de-cox",
     "title": "42  Analyse de survie",
     "section": "\n42.4 Analyse multivariable (modèle de Cox)",
-    "text": "42.4 Analyse multivariable (modèle de Cox)\nPour une analyse multivariable, on aura recours à un modèle de Cox. Il est tout à fait possible d’inclure des variables continues, par exemple l’âge des patients. Ici, nous n’abordons qu’un modèle avec des variables fixes au cours du temps. Mais il est possible de calculer un modèle de Cox avec des variables explicatives pouvant changer au cours du temps.\nLe modèle de Cox se calcule avec survival::coxph().\n\nmod &lt;- coxph(\n  Surv(ttdeath, death) ~ stage + trt + age,\n  data = trial\n)\n\n\n42.4.1 Visualisation des résultats\nComme pour la régression logistique (cf. Chapitre 22), nous pouvons utiliser gtsummary::tbl_regression(), ggstats::ggcoef_model() et ggstats::ggcoef_table() pour afficher les coefficients du modèle.\nIl est pertinent d’afficher l’exponentielle des coefficients car il s’interprètent comme des risque relatifs ou hazard ratios.\n\nmod |&gt; \n  tbl_regression(exponentiate = TRUE) |&gt; \n  add_global_p(keep = TRUE) |&gt; \n  bold_labels()\n\n\n\n\n\n\n\n\n\n\n\n\nCaractéristique\n\nHR\n1\n\n\n95% IC\n1\n\np-valeur\n\n\n\nT Stage\n\n\n0,002\n\n\n    T1\n—\n—\n\n\n\n    T2\n1,31\n0,74 – 2,30\n0,4\n\n\n    T3\n1,18\n0,64 – 2,16\n0,6\n\n\n    T4\n2,72\n1,58 – 4,68\n&lt;0,001\n\n\nChemotherapy Treatment\n\n\n0,12\n\n\n    Drug A\n—\n—\n\n\n\n    Drug B\n1,37\n0,93 – 2,02\n0,12\n\n\nAge\n1,01\n1,00 – 1,02\n0,15\n\n\n\n\n1\nHR = rapport de risques instantanés, IC = intervalle de confiance\n\n\n\n\n\n\n\n\nlibrary(ggstats)\nmod |&gt; \n  ggcoef_model(exponentiate = TRUE)\n\n\n\n\n\n\n\n\nmod |&gt; \n  ggcoef_table(exponentiate = TRUE)\n\n\n\n\n\n\n\nLe risque relatif correspond au rapport des risques instantanés. Il est par exemple de 2,7 pour les personnes au stade T4 par rapport à celles au stade T1. Cela signifie donc qu’à tout moment le risque de décéder est 2,7 fois plus important pour les personnes au stade 4 par rapport à celles au stade 1.\n\n42.4.2 Vérification des hypothèses\nLe modèle de Cox considère que, bien que le risque de décès évolue au cours du temps, les risques relatifs restent quant à eux identiques. Il s’agit de l’hypothèse de la proportionnalité des risques relatifs. Selon cette hypothèse les résidus de Schoenfeld ne dépendent pas du temps. Cette hypothèse peut être testée avec la fonction cox.zph().\n\nmod |&gt; cox.zph()\n\n       chisq df    p\nstage  0.983  3 0.81\ntrt    1.863  1 0.17\nage    0.159  1 0.69\nGLOBAL 2.969  5 0.70\n\n\nUn test est ici effectué globalement et pour chaque variable individuellement. Une valeur de p inférieure à 5% indique un problème potentiel en termes de proportionnalité des risques.\nLa fonction survminer::ggcoxzph() du package survminer permet de représenter les résidus de Schoenfeld et de visualiser si leur répartition est relativement stable au cours du temps (visuellement horizontale autour de 0) ou bien s’ils suivent une autre distribution.\n\nmod |&gt; cox.zph() |&gt; survminer::ggcoxzph()\n\n\n\n\n\n\n\n\n42.4.3 Représentation graphique d’une courbe de survie ajustée\nIl est possible de calculer, avec survfit2(), des courbes de survie ajustées à partir d’un modèle de Cox. Cependant, pour indiquer la variable de stratification des courbes, il est nécessaire de l’inclure avec strata() dans l’appel du modèle de Cox.\n\ntrial |&gt; \n  coxph(\n    Surv(ttdeath, death) ~ strata(stage) + trt + age,\n    data = _\n  ) |&gt; \n  survfit2() |&gt; \n  ggsurvfit()\n\n\n\n\n\n\n\n\n42.4.4 Modèles de Cox univariables\nComme déjà abordé dans le chapitre sur la régression logistique binaire, il est possible d’effectuer en une fois plusieurs modèles univariables avec gtsummary::tbl_uvregression() (cf. Section 22.9).\n\ntrial |&gt; \n  tbl_uvregression(\n    method = coxph,\n    y = Surv(ttdeath, death),\n    include = c(stage, trt, age),\n    exponentiate = TRUE\n  ) |&gt; \n  bold_labels()\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaractéristique\nN\n\nHR\n1\n\n\n95% IC\n1\n\np-valeur\n\n\n\nT Stage\n200\n\n\n\n\n\n    T1\n\n—\n—\n\n\n\n    T2\n\n1,18\n0,68 – 2,04\n0,6\n\n\n    T3\n\n1,23\n0,69 – 2,20\n0,5\n\n\n    T4\n\n2,48\n1,49 – 4,14\n&lt;0,001\n\n\nChemotherapy Treatment\n200\n\n\n\n\n\n    Drug A\n\n—\n—\n\n\n\n    Drug B\n\n1,25\n0,86 – 1,81\n0,2\n\n\nAge\n189\n1,01\n0,99 – 1,02\n0,3\n\n\n\n\n1\nHR = rapport de risques instantanés, IC = intervalle de confiance",
+    "text": "42.4 Analyse multivariable (modèle de Cox)\nPour une analyse multivariable, on aura recours à un modèle de Cox. Il est tout à fait possible d’inclure des variables continues, par exemple l’âge des patients. Ici, nous n’abordons qu’un modèle avec des variables fixes au cours du temps. Mais il est possible de calculer un modèle de Cox avec des variables explicatives pouvant changer au cours du temps.\nLe modèle de Cox se calcule avec survival::coxph().\n\nmod &lt;- coxph(\n  Surv(ttdeath, death) ~ stage + trt + age,\n  data = trial\n)\n\n\n42.4.1 Visualisation des résultats\nComme pour la régression logistique (cf. Chapitre 22), nous pouvons utiliser gtsummary::tbl_regression(), ggstats::ggcoef_model() et ggstats::ggcoef_table() pour afficher les coefficients du modèle.\nIl est pertinent d’afficher l’exponentielle des coefficients car il s’interprètent comme des risque relatifs ou hazard ratios.\n\nmod |&gt; \n  tbl_regression(exponentiate = TRUE) |&gt; \n  add_global_p(keep = TRUE) |&gt; \n  bold_labels()\n\n\n\n\n\n\n\n\n\n\n\n\nCaractéristique\n\nHR\n1\n\n\n95% IC\n1\n\np-valeur\n\n\n\nT Stage\n\n\n0,002\n\n\n    T1\n—\n—\n\n\n\n    T2\n1,31\n0,74 – 2,30\n0,4\n\n\n    T3\n1,18\n0,64 – 2,16\n0,6\n\n\n    T4\n2,72\n1,58 – 4,68\n&lt;0,001\n\n\nChemotherapy Treatment\n\n\n0,12\n\n\n    Drug A\n—\n—\n\n\n\n    Drug B\n1,37\n0,93 – 2,02\n0,12\n\n\nAge\n1,01\n1,00 – 1,02\n0,15\n\n\n\n\n1\nHR = rapport de risques instantanés, IC = intervalle de confiance\n\n\n\n\n\n\n\n\nlibrary(ggstats)\nmod |&gt; \n  ggcoef_model(exponentiate = TRUE)\n\n\n\n\n\n\n\n\nmod |&gt; \n  ggcoef_table(exponentiate = TRUE)\n\n\n\n\n\n\n\nLe risque relatif correspond au rapport des risques instantanés. Il est par exemple de 2,7 pour les personnes au stade T4 par rapport à celles au stade T1. Cela signifie donc qu’à tout moment le risque de décéder est 2,7 fois plus important pour les personnes au stade 4 par rapport à celles au stade 1.\n\n42.4.2 Vérification des hypothèses\nLe modèle de Cox considère que, bien que le risque de décès évolue au cours du temps, les risques relatifs restent quant à eux identiques. Il s’agit de l’hypothèse de la proportionnalité des risques relatifs. Selon cette hypothèse les résidus de Schoenfeld ne dépendent pas du temps. Cette hypothèse peut être testée avec la fonction cox.zph().\n\nmod |&gt; cox.zph()\n\n       chisq df    p\nstage  0.983  3 0.81\ntrt    1.863  1 0.17\nage    0.159  1 0.69\nGLOBAL 2.969  5 0.70\n\n\nUn test est ici effectué globalement et pour chaque variable individuellement. Une valeur de p inférieure à 5% indique un problème potentiel en termes de proportionnalité des risques.\nLa fonction survminer::ggcoxzph() du package survminer permet de représenter les résidus de Schoenfeld et de visualiser si leur répartition est relativement stable au cours du temps (visuellement horizontale autour de 0) ou bien s’ils suivent une autre distribution.\n\nmod |&gt; cox.zph() |&gt; survminer::ggcoxzph()\n\n\n\n\n\n\n\n\n42.4.3 Représentation graphique d’une courbe de survie ajustée\nIl est possible de calculer, avec survfit2(), des courbes de survie ajustées à partir d’un modèle de Cox. Cependant, pour indiquer la variable de stratification des courbes, il est nécessaire de l’inclure avec strata() dans l’appel du modèle de Cox.\n\ntrial |&gt; \n  coxph(\n    Surv(ttdeath, death) ~ strata(stage) + trt + age,\n    data = _\n  ) |&gt; \n  survfit2() |&gt; \n  ggsurvfit()\n\n\n\n\n\n\n\n\n42.4.4 Sélection pas à pas d’un modèle réduit\nPour réduire le modèle (voir Chapitre 23), il est possible d’utiliser la fonction step().\n\nmod |&gt; step()\n\nStart:  AIC=1002.04\nSurv(ttdeath, death) ~ stage + trt + age\n\n\nError in drop1.default(fit, scope$drop, scale = scale, trace = trace, : le nombre de lignes utilisées a changé : supprimer les valeurs manquantes ?\n\n\nDans notre exemple, nous rencontrons une erreur liée à des valeurs manquantes pour l’une des variables explicatives. Comme abordé précédemment (cf. Section 23.8), nous pouvons avoir recours à la fonction step_with_na() de notre cru.\n\nstep_with_na &lt;- function(model,\n                         full_data = NULL, ...) {\n  # data with no NAs\n  if (is.null(full_data)) {\n    ...data_no_na &lt;- model.frame(model)\n  } else {\n    ...data_no_na &lt;- get_all_vars(model, data = full_data)\n    ...data_no_na &lt;- ...data_no_na[complete.cases(...data_no_na), ]\n  }\n  \n  # assign ...data_no_na in parent.frame()\n  assign(\"...data_no_na\", ...data_no_na, envir = parent.frame())\n  \n  # refit the model without NAs\n  model_no_na &lt;- update(\n    model,\n    formula = terms(model),\n    data = ...data_no_na\n  )\n  \n  # apply step()\n  model_simplified &lt;- step(model_no_na, ...)\n  \n  # recompute simplified model using full data\n  if (is.null(full_data)) {\n    update(\n      model,\n      formula = terms(model_simplified)\n    )\n  } else {\n    update(\n      model,\n      formula = terms(model_simplified),\n      data = full_data\n    )\n  }\n}\n\nIl y a néanmoins une petite subtilité pour l’utiliser ici. En effet, elle a besoin de pouvoir reconstruire notre outcome qui est ici une variable composée créée avec Surv(). Deux possibilités. La première consiste à transmettre à step_with_na() le jeu de données complet.\n\nmod |&gt; step_with_na(full_data = trial)\n\nStart:  AIC=1002.04\nSurv(ttdeath, death) ~ stage + trt + age\n\n        Df    AIC\n&lt;none&gt;     1002.0\n- age    1 1002.1\n- trt    1 1002.5\n- stage  3 1011.2\n\n\nCall:\ncoxph(formula = Surv(ttdeath, death) ~ stage + trt + age, data = full_data)\n\n              coef exp(coef) se(coef)     z        p\nstageT2   0.268596  1.308126 0.287412 0.935 0.350028\nstageT3   0.164980  1.179370 0.309897 0.532 0.594468\nstageT4   1.000324  2.719163 0.276830 3.614 0.000302\ntrtDrug B 0.312336  1.366614 0.199005 1.569 0.116535\nage       0.010305  1.010358 0.007154 1.440 0.149753\n\nLikelihood ratio test=17.98  on 5 df, p=0.002975\nn= 189, number of events= 103 \n   (11 observations effacées parce que manquantes)\n\n\nLa seconde consiste à créer notre outcome en amont du calcul du modèle.\n\ntrial &lt;- trial |&gt; \n  mutate(survie = Surv(ttdeath, death))\nmod2 &lt;- coxph(\n  survie ~ stage + trt + age,\n  data = trial\n)\nmod2 |&gt; step_with_na()\n\nStart:  AIC=1002.04\nsurvie ~ stage + trt + age\n\n        Df    AIC\n&lt;none&gt;     1002.0\n- age    1 1002.1\n- trt    1 1002.5\n- stage  3 1011.2\n\n\nCall:\ncoxph(formula = survie ~ stage + trt + age, data = trial)\n\n              coef exp(coef) se(coef)     z        p\nstageT2   0.268596  1.308126 0.287412 0.935 0.350028\nstageT3   0.164980  1.179370 0.309897 0.532 0.594468\nstageT4   1.000324  2.719163 0.276830 3.614 0.000302\ntrtDrug B 0.312336  1.366614 0.199005 1.569 0.116535\nage       0.010305  1.010358 0.007154 1.440 0.149753\n\nLikelihood ratio test=17.98  on 5 df, p=0.002975\nn= 189, number of events= 103 \n   (11 observations effacées parce que manquantes)\n\n\n\n42.4.5 Modèles de Cox univariables\nComme déjà abordé dans le chapitre sur la régression logistique binaire, il est possible d’effectuer en une fois plusieurs modèles univariables avec gtsummary::tbl_uvregression() (cf. Section 22.9).\n\ntrial |&gt; \n  tbl_uvregression(\n    method = coxph,\n    y = Surv(ttdeath, death),\n    include = c(stage, trt, age),\n    exponentiate = TRUE\n  ) |&gt; \n  bold_labels()\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaractéristique\nN\n\nHR\n1\n\n\n95% IC\n1\n\np-valeur\n\n\n\nT Stage\n200\n\n\n\n\n\n    T1\n\n—\n—\n\n\n\n    T2\n\n1,18\n0,68 – 2,04\n0,6\n\n\n    T3\n\n1,23\n0,69 – 2,20\n0,5\n\n\n    T4\n\n2,48\n1,49 – 4,14\n&lt;0,001\n\n\nChemotherapy Treatment\n200\n\n\n\n\n\n    Drug A\n\n—\n—\n\n\n\n    Drug B\n\n1,25\n0,86 – 1,81\n0,2\n\n\nAge\n189\n1,01\n0,99 – 1,02\n0,3\n\n\n\n\n1\nHR = rapport de risques instantanés, IC = intervalle de confiance",
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       "**Analyses avancées**",
       "<span class='chapter-number'>42</span>  <span class='chapter-title'>Analyse de survie</span>"