{cases} is an R package to simultaneously assess classification accuracy of multiple classifiers in several subgroups (strata). For instance, it allows to asses the accuracy of multiple candidate (index) diagnostic tests which is often measured with
- sensitivity (accuracy in the diseased subgroup) and
- specificity (accuracy in the healthy subgroup).
A widespread goal in diagnostic accuracy studies a so-called co-primary analysis of these two endpoints, i.e. to show a significant benefit (compared to some benchmark) in sensitivity and specificity for at least one of the candidate classifiers. The package implements different methods for multiplicity adjustment for that purpose (e.g. Bonferroni, maxT, pairs bootstrap). Theoretical background and an extensive simulation study is explained in the paper by Westphal & Zapf (2024).
To install the latest stable version from CRAN, use the following R command:
install.packages("cases")
You can install the latest development version from GitHub with:
# install.packages("remotes")
remotes::install_github("maxwestphal/cases",
ref = "development",
build_vignettes = TRUE
)
A vignette which explains the basic functionality of the {cases} package can be displayed as follows:
vignette(topic = "package_overview", package = "cases")
The following vignette shows an exemplary usage of the package in the context of biomarker assessment and prediction model evaluation:
vignette(topic = "example_wdbc", package = "cases")
- Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). doi:10.1177/09622802241236933