Package: WR 1.0.1

WR: Win Ratio Analysis of Composite Time-to-Event Outcomes

Implements various win ratio methodologies for composite endpoints of death and non-fatal events, including the (stratified) proportional win-fractions (PW) regression models (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 2021 <doi:10.1111/biom.13501>).

Authors:Lu Mao and Tuo Wang

WR_1.0.1.tar.gz
WR_1.0.1.zip(r-4.7)WR_1.0.1.zip(r-4.6)WR_1.0.1.zip(r-4.5)
WR_1.0.1.tgz(r-4.6-any)WR_1.0.1.tgz(r-4.5-any)
WR_1.0.1.tar.gz(r-4.7-any)WR_1.0.1.tar.gz(r-4.6-any)
WR_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
WR/json (API)

# Install 'WR' in R:
install.packages('WR', repos = c('https://lmaowisc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lmaowisc/wr/issues

Pkgdown/docs site:https://lmaowisc.github.io

Datasets:
  • gbc - A subset of the German Breast Cancer study data
  • hfaction_cpx9 - A subset of the HF-ACTION study data on high-risk non-ischemic heart failure patients
  • non_ischemic - A subset of the HF-ACTION study data on non-ischemic heart failure patients with full covariate measurement.

On CRAN:

Conda:

5.53 score 56 scripts 238 downloads 3 mentions 8 exports 26 dependencies

Last updated from:6ad64b52aa. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING161
source / vignettesOK234
linux-release-x86_64WARNING155
macos-release-arm64WARNING106
macos-oldrel-arm64WARNING90
windows-develWARNING113
windows-releaseWARNING126
windows-oldrelWARNING120
wasm-releaseOK107

Exports:basegumbel.estpwregpwreg1score.procscores.pwreg1WRrecWRSS

Dependencies:clicpp11cubaturedplyrgenericsgluegumbellatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6Rcpprlangstringistringrsurvivaltibbletidyrtidyselectutf8vctrswithr

Proportional win-fractions (PW) regression of composite endpoints of death and nonfatal event
MODEL SPECIFICATION | BASIC SYNTAX | AN EXAMPLE WITH THE HF-ACTION TRIAL | References
Rendered fromPW_reg.Rmdusingknitr::rmarkdown

Last update: 2024-12-07
Started: 2024-12-07

Sample size calculation for standard win ratio test
INTRODUCTION | Data and the test | Methods for sample size calculation | BASIC SYNTAX | A REAL EXAMPLE | Pilot data description | Use pilot data to estimate baseline parameters | Using WRSS() to compute sample size | References
Rendered fromWR_sample_size.Rmdusingknitr::rmarkdown

Last update: 2024-12-07
Started: 2024-12-07

Stratified proportional win-fractions (PW) regression of composite endpoints of death and nonfatal event
MODEL & INFERENCE | Outcome data and modeling target | Model specification | Number of strata and inference procedure | BASIC SYNTAX | AN EXAMPLE WITH THE GERMAN BREAST CANCER STUDY | Data preparation | Stratification by menopause status | Stratification by age | References
Rendered fromPW_stratify_reg.Rmdusingknitr::rmarkdown

Last update: 2024-12-07
Started: 2024-12-07

Two-sample win ratio tests of possibly recurrent event and death
INTRODUCTION | Data | General framework for hypotheses and tests | Choice of win function | BASIC SYNTAX | AN EXAMPLE WITH THE HF-ACTION TRIAL | Data description | Win ratio tests on recurrent event and death | Comparison with standard win ratio | References
Rendered fromWR_test_rec.Rmdusingknitr::rmarkdown

Last update: 2024-12-07
Started: 2024-12-07