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README.Rmd
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README.Rmd
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---
output: "github_document"
---
```{r, include=FALSE}
shieldio_badge <- function(name, text, color, hover = NULL, href = NULL, ...,
url = 'https://img.shields.io/badge/{{name}}-{{text}}-{{color}}.svg',
style = 'flat-square') {
.dots <- list(...)
name <- RCurl::curlEscape(whisker::whisker.render(name, .dots))
text <- RCurl::curlEscape(whisker::whisker.render(text, .dots))
color <- whisker::whisker.render(color, .dots)
if (is.numeric(c <- type.convert(color)))
color <- switch(letters[[floor(pmax(pmin(c, 1), 0) * 6 + 1)]],
g = 'brightgreen',
f = 'brightgreen',
e = 'green',
d = 'yellowgreen',
c = 'yellow',
b = 'orange',
a = 'red')
color <- RCurl::curlEscape(color)
url <- whisker::whisker.render(paste(url, paste0('style=', style), sep = '?'))
hover <- if (is.null(hover)) name else hover
whisker::whisker.render('[]({{href}})')
}
```
```{r build.badges, include=FALSE}
failed_tests = tryCatch(devtools::test(), error = function(e) NULL)
failed_tests = if (!is.null(failed_tests) && 'failed' %in% names(as.data.frame(failed_tests))) sum(failed_tests$failed) else 1
build_badge <- shieldio_badge(
'build', '{{p}}', '{{d}}', 'Build Status',
p = if (failed_tests == 0) 'passing' else 'failing',
d = if (failed_tests == 0) 'brightgreen' else 'red')
coverage = covr::percent_coverage(covr::package_coverage());
coverage_badge <- shieldio_badge(
'coverage', '{{p}}%', '{{d}}', 'Code Coverage',
p = round(coverage,1),
d = (coverage/100-0.8)/0.2)
```
# gClinBiomarker
`r build_badge` `r coverage_badge`
## Overview
gClinBiomarker is an R package that contains functions to perform baseline and longitutinal biomarker analyses
Source code: https://github.roche.com/Rpackages/gClinBiomarker
Project page: https://pages.github.roche.com/Rpackages/gClinBiomarker/
Documentations could be found under the "article" tab of the project page. Two documents are provided:
- A user vignette demonstrates how gClinBiomarker package may help in your biomarker analysis
- An exapmle use case document which contains more detailed use cases
pdf version of these two documents can be found [here](https://github.roche.com/Rpackages/gClinBiomarker/tree/master/inst/doc).
gClinBiomarker also provide a set of Rmarkdown templates that allow you to generate biomarker analysis report by "one click". The R markdown templates can be found at [here](https://github.roche.com/lengn/gClinbiomarker_documents), along with some slide decks.
## Getting Started
### Installation
To install this package from R, use `install_github()` function from the **devtools** package
In R, type:
```{r, eval = FALSE}
## install.packages("devtools")
library(devtools)
install_github("RPackages/gClinBiomarker", host="https://github.roche.com/api/v3")
```
Note that on bce (r.bas.roche.com), the default R is from an older version. To install, type
```{r, eval = FALSE}
install_github("RPackages/gClinBiomarker", host="github.roche.com/api/v3")
```
### Getting the Documentation
Use the command `vignette(package = 'gClinBiomarker')` to view a list of
avaialble vignettes for the `gClinBiomarker` package. Then use the command
`vignette(<vignett_name>)` to view the relevant documentation.
## gClinBiomarker Analysis and Workflows
### Supported endpoint and biomarker types
#### Endpoints
* Time-to-event
* Binary
* Continuous
* Longitudinal
#### Biomarkers
* Continuous
* Ordinal
* Categorical
* Longitudinal
#### Analysis
* 2-arm study: predictive and prognostic biomarker analysis
* 1-arm study: prognostic biomarker analysis
### Workflow
#### Step 1:
Is your biomarker population your full patient population?
Take a look at these functions:
`SummaryVars()`, `CompareKM()`, `PlotRspBar()`
#### Step 2:
Is your biomarker response a dynamic range?
Does it have a skewed distribution?
Is it correlated with clinical variables?
`PlotProperty()`, `PlotTabForestMulti()`
#### Step 3:
Is your biomarker response associated with a clinical outcome?
Is it prognostic or predictive?
Is there an optimal biomarker cutoff with a consistent trend?
`PlotTabForestBiomarker()`, `PlotSTEPP()`
#### Step 4:
Estimate the clinical benefit within a biomarker subgroup.
`PlotKM()`, `PlotLong()`, `PlotRspBar()`, `CoxTab()`, `LogRankTab()`
```{r, include = FALSE}
# 
# 
# 
# 
# 
```