average.power.coxph: Compute the average power of many Cox regression models

In FDRsamplesize2: Computing Power and Sample Size for the False Discovery Rate in Multiple Applications

Compute the average power of many Cox regression models

Description

Compute the average power of many Cox regression models for a given number of events, p-value threshold, vector of effect sizes (log hazard ratio),and variance of predictor variables

Usage

average.power.coxph(n, alpha, logHR, v)

Arguments

n	number of events (scalar)
alpha	p-value threshold (scalar)
logHR	log hazard ratio (vector)
v	variance of predictor variable (vector)

Value

Average power estimate for multiple testing procedure

References

Hsieh, FY and Lavori, Philip W (2000) Sample-size calculations for the Cox proportional hazards regression model with non-binary covariates. Controlled Clinical Trials 21(6):552-560.

See Also

power.cox for more details about power calculation of single-predictor Cox regression model. The power calculation is based on asymptotic normal approximation.

Examples

logHR = log(rep(c(1, 2),c(900, 100)));
v = rep(1, 1000);
average.power.coxph(n = 50, alpha = 0.05, logHR = logHR, v = v)

FDRsamplesize2 documentation built on May 29, 2024, 7:12 a.m.