Sample Size and Power Calculation for Common Non-Parametric Tests in Survival Analysis

A number of statistical tests have been proposed to compare two survival curves, including the difference in (or ratio of) t-year survival, difference in (or ratio of) p-th percentile survival, difference in (or ratio of) restricted mean survival time, and the weighted log-rank test. Despite the multitude of options, the convention in survival studies is to assume proportional hazards and to use the unweighted log-rank test for design and analysis. This package provides sample size and power calculation for all of the above statistical tests with allowance for flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, mixture cure). It is the companion R package to the paper by Yung and Liu (2019) . Specific to the weighted log-rank test, users may specify which approximations they wish to use to estimate the large-sample mean and variance. The default option has been shown to provide substantial improvement over the conventional sample size and power equations based on Schoenfeld (1981) .


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1.0.1 by Godwin Yung, a year ago

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Authors: Godwin Yung [aut, cre] , Yi Liu [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports stats, utils

Suggests knitr, rmarkdown, tidyverse, ggplot2

See at CRAN