# OneSide.varyEffect: One-Sided Tests with varying effect sizes In TrialSize: R functions in Chapter 3,4,6,7,9,10,11,12,14,15

One-sided tests

Ho: δ_j = 0

Ha: δ_j > 0

## Usage

 1 OneSide.varyEffect(s1, s2, m, m1, delta, a1, r1, fdr)

## Arguments

 s1 We use bisection method to find the sample size, which let the equation h(n)=0. Here s1 and s2 are the initial value, 00, ~j~ in~ M1=effect size for prognostic genes. a1 a1 is the allocation proportion for group 1. a2=1-a1. r1 r1 is the number of true rejection fdr fdr is the FDR level.

## Details

alpha_star=r1*fdr/((m-m1)*(1-fdr)), which is the marginal type I error level for r1 true rejection with the FDR controlled at f.

beta_star=1-r1/m1, which is equal to 1-power.

## References

Chow SC, Shao J, Wang H. Sample Size Calculation in Clinical Research. New York: Marcel Dekker, 2003

## Examples

 1 2 3 4 5 delta=c(rep(1,40/2),rep(1/2,40/2)); Example.12.2.2 <- OneSide.varyEffect(100,150,4000,40,delta,0.5,24,0.01) Example.12.2.2 # n=148 s1

TrialSize documentation built on May 30, 2017, 7:18 a.m.