n.fdr.coxph {FDRsamplesize2}

Sample size calculation for the Cox proportional hazards regression model

Description

Find number of events needed to have a desired false discovery rate and average power for a large number of Cox regression models with non-binary covariates.

Usage

n.fdr.coxph(fdr, pwr, logHR, v, pi0.hat = "BH")

Arguments

fdr

desired FDR (scalar numeric)

pwr

desired average power (scalar numeric)

logHR

log hazard ratio (vector)

v

variance of predictor variable (vector)

pi0.hat

method to estimate proportion pi0 of tests with true null, including: "HH" (p-value histogram height), "HM" (p-value histogram mean), "BH" (Benjamini & Hochberg 1995), "Jung" (Jung 2005)

Value

A list with the following components:

n

number of events estimate

computed.avepow

average power

desired.avepow

desired average power

desired.fdr

desired FDR

input.pi0

proportion of tests with a true null hypothesis

alpha

fixed p-value threshold for multiple testing procedure

n.its

number of iteration

max.its

maximum number of iteration, default is 50

n0

lower limit for initial sample size range

n1

upper limit for initial sample size range

Note

For the test with power calculation based on asymptotic normal approximation, we suggest checking FDRsamplesize2 calculation by simulation.

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.

Examples

log.HR=log(rep(c(1,2),c(900,100)))
v=rep(1,1000)
n.fdr.coxph(fdr=0.1, pwr=0.8,logHR=log.HR, v=v, pi0.hat="BH")