powerWO: Power calculation for the win odds test (no ties) In hce: Design and Analysis of Hierarchical Composite Endpoints

 powerWO R Documentation

Power calculation for the win odds test (no ties)

Description

Power calculation for the win odds test (no ties)

Usage

``````powerWO(N, WO, SD = NULL, k = 0.5, alpha = 0.05, WOnull = 1)
``````

Arguments

 `N` a numeric vector of sample size values. `WO` the given win odds for the alternative hypothesis. A numeric vector of length 1. `SD` assumed standard deviation of the win proportion. By default uses the conservative SD. A numeric vector of length 1. `k` proportion of active group in the overall sample size. Default is 0.5 (balanced randomization). A numeric vector of length 1. `alpha` the significance level for the 2-sided test. Default is 0.05. A numeric vector of length 1. `WOnull` the win odds value of the null hypothesis (default is 1). A numeric vector of length 1.

Value

a data frame containing the calculated power with input values.

References

Gasparyan SB et al. (2021) "Power and sample size calculation for the win odds test: application to an ordinal endpoint in COVID-19 trials." Journal of Biopharmaceutical Statistics 31.6 : 765-787. doi:10.1080/10543406.2021.1968893

`sizeWO()`, `minWO()` for WO sample size or minimum detectable WO calculation.

Examples

``````# Example 1- Use the default standard deviation
powerWO(N = 1000, WO = 1.2)
powerWO(N = seq(500, 1500, 100), WO = 1.2)
# Example 2 - Use data-driven win odds and standard deviation from the COVID19 dataset
res <- calcWO(x = COVID19, AVAL = "GROUP", TRTP = "TRTP", ref = "Placebo")
print(res)
powerWO(N = 500, WO = res\$WO, SD = res\$SD_WP)
powerWO(N = 500, WO = res\$WO) # power with the default standard deviation for the win proportion.
# Example 3 - Non-balanced 3:1 randomization
powerWO(N = 1000, WO = 1.2, k = 0.75)
``````

hce documentation built on May 29, 2024, 5:52 a.m.