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WR (version 1.0)

Win Ratio Analysis of Composite Time-to-Event Outcomes

Description

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 ), (stratified) two-sample tests with possibly recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 2021 ).

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Version

Install

install.packages('WR')

Monthly Downloads

172

Version

1.0

License

GPL (>= 2)

Maintainer

Lu Mao

Last Published

November 26th, 2021

Functions in WR (1.0)

WRrec

Generalized win ratio tests
print.pwreg

Print the results of the proportional win-fractions regression model
hfaction_cpx9

A subset of the HF-ACTION study data on high-risk non-ischemic heart failure patients
base

Compute the baseline parameters needed for sample size calculation for standard win ratio test
print.WRrec

Print the results of the two-sample recurrent-event win ratio analysis
gbc

A subset of the German Breast Cancer study data
WRSS

Compute the sample size for standard win ratio test
gumbel.est

Estimate baseline parameters in the Gumbel--Hougaard model for sample size calculation using pilot data
non_ischemic

A subset of the HF-ACTION study data on non-ischemic heart failure patients with full covariate measurement.
plot.pwreg.score

Plot the standardized score processes
score.proc

Computes the standardized score processes
print.pwreg.score

Print information on the content of the pwreg.score object
pwreg

Fit a standard proportional win-fractions (PW) regression model