fedesign: Trial Designs Based On Fisher's Exact Test

In clinfun: Clinical Trial Design and Data Analysis Functions

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)
mdrr(n, cprob, presp, alpha=0.05, power=0.8, niter=15)
fe.power(d, n1, n2, p1, alpha = 0.05)
or2pcase(pcontrol, OR)

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
n	total number of subjects
cprob	proportion of patients who are marger positive
presp	probability of response in all subjects
niter	number of iterations in binary search
OR	odds-ratio

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.

Since Fisher's exact test orders the tables by their probability the test is naturally two-sided.

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.

mdrr computes the minimum detectable P(resp|marker+) and P(resp|marker-) configurations when total sample size (n), P(response) (presp) and proportion of subjects who are marker positive (cprob) are specified.

or2pcase give the probability of disease among the cases for a given probability of disease in controls (pcontrol) and odds-ratio (OR).

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.

clinfun documentation built on Oct. 20, 2023, 1:07 a.m.