fedesign {clinfun} | R Documentation |
Calculates sample size, effect size and power based on Fisher's exact test
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)
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 |
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.
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.