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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# acdcourse: Course on Analyzing Cohort Datasets with R
<!-- badges: start -->
[](https://travis-ci.org/lwjohnst86/acdcourse)
[](https://doi.org/10.5281/zenodo.3247819)
<!-- badges: end -->
Cohort studies are a powerful study design that allows researchers to better
understand how to reduce, manage, or prevent disease in a population. The
findings from cohorts are critical to creating effective public health
interventions. Because peoples' lives and health are directly impacted by the
findings from cohort studies, there is immense pressure to ensure that the
analysis done correctly and appropriately and that the presentation of results
is as meaningful and as simple as possible. The difficulty in the analysis also
comes from the wide range in study designs, data collection and type, and
research questions. In this course, we'll be covering how and what to
analytically ask of cohort data, what are special considerations for data
processing, which statistical techniques to choose, and how to present the
results for effective communication to public health professionals.
## Installation and usage
You can install acdcourse from GitHub with
``` r
# install.packages("remotes")
remotes::install_github("lwjohnst86/acdcourse")
```
To work through each chapter, run these commands:
``` r
learnr::run_tutorial("chapter1", package = "acdcourse")
learnr::run_tutorial("chapter2", package = "acdcourse")
learnr::run_tutorial("chapter3", package = "acdcourse")
learnr::run_tutorial("chapter4", package = "acdcourse")
```
## General course outline
- Chapter 1: Introduction to cohorts and to analyzing them
- Lesson 1: Introduction to cohort designs
- Lesson 2: Cohort types, variables, and the Framingham Study
- Lesson 3: Prevalence and incidence in cohorts
- Chapter 2: Exploring, wrangling, and transforming cohort data
- Lesson 1: Pre-wrangling exploration
- Lesson 2: Discrete data and tidying it for later analysis
- Lesson 3: Variable transformations
- Chapter 3: Statistical methods for cohort data
- Lesson 1: Statistical analyses for cohort studies
- Lesson 2: Adjustment, confounding, and model building
- Lesson 3: Testing for interactions and sensitivity analyses
- Lesson 4: Tidying and interpreting model results
- Chapter 4: Presentation of results from cohort analyses
- Lesson 1: Presenting cohort findings is tricky, be careful
- Lesson 2: Communicating cohort findings through graphs
- Lesson 3: Use tables effectively to show your results
The dataset used is:
- `framingham`: Framingham Heart Study.
## Learner persona
This course will be useful / primarily targetted to three (hypothetical)
learners:
* Catherine: She will likely already know most of the basic statistical
techniques in the course. However, this course may give her material on creating
her own course, as this course will present various types of cohort study designs
and how to interpret the results from the statistical analyses (in the cohort
context). This will be useful to her medical students, who rely on this type of
data to make medical decisions that could save people's lives.
* Jamie: The material in this course will teach her some fundamentals on how to
apply statistical techniques using code. If she teaches health insurance policy
research at her alma mater, knowing the specifics of analyzing cohort datasets
would help make her students better with creating/analyzing/studying health
insurance policies.
* Moe: He will likely not be interested in the statistical portion of this
course, though he may find the sections with the code to analyze and interpret
the cohort data useful. If he was in any biomedical or health sciences graduate
programs, he would find this course very useful.
## Learning Objectives
There general learning objectives are to:
- Learn how cohort data looks like and what questions to ask of it
- Learn what to look for when exploring and pre-processing the cohort data
before analyzing it
- Learn how to think about and approach processing, analyzing, and presenting
results from cohort studies
- Approach analyzing cohort in a way that allows the results to be meaningful
and interpretable to main users, the clinicians and the public health
professionals
## Prerequisite or familiar knowledge / concepts
- Tidyverse R packages
- Descriptive epidemiology
- Statistics:
- Correlation and regression
- Multiple and Logistic Regression
- Mixed Effects Models
- Network Analysis
<!-- TODO: Include links to some of these resources -->
## Citing this learning resource
See:
```{r}
citation("acdcourse")
```