fedesign {clinfun}R Documentation

Trial Designs Based On Fisher's Exact Test

Description

Calculates sample size, effect size and power based on Fisher's exact test

Usage

fe.ssize(p1,p2,alpha=0.05,power=0.8,r=1,npm=5,mmax=1000)
CPS.ssize(p1,p2,alpha=0.05,power=0.8,r=1)
fe.mdor(ncase,ncontrol,pcontrol,alpha=0.05,power=0.8)
fe.power(d, n1, n2, p1, alpha = 0.05)

Arguments

p1 response rate of standard treatment
p2 response rate of experimental treatment
d difference = p2-p1
pcontrol control group probability
n1 sample size for the standard treatment group
n2 sample size for the standard treatment group
ncontrol control group sample size
ncase case group sample size
alpha size of the test (default 5%)
power power of the test (default 80%)
r treatments are randomized in 1:r ratio (default r=1)
npm the sample size program searches for sample sizes in a range (+/- npm) to get the exact power
mmax the maximum group size for which exact power is calculated

Details

CPS.ssize returns Casagrande, Pike, Smith sample size which is a very close to the exact. Use this for small differences p2-p1 (hence large sample sizes) to get the result instantaneously.

fe.ssize return a 2x3 matrix with CPS and Fisher's exact sample sizes with power.

fe.mdor return a 3x2 matrix with Schlesselman, CPS and Fisher's exact minimum detectable odds ratios and the corresponding power.

fe.power returns a Kx2 matrix with probabilities (p2) and exact power.

References

Casagrande, JT., Pike, MC. and Smith PG. (1978). An improved approximate formula for calculating sample sizes for comparing two binomial distributions. Biometrics 34, 483-486.

Fleiss, J. (1981) Statistical Methods for Rates and Proportions.

Schlesselman, J. (1987) Re: Smallest Detectable Relative Risk With Multiple Controls Per Case. Am. J. Epi.


[Package clinfun version 0.8.4 Index]