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)
mdrr(n, cprob, presp, alpha=0.05, power=0.8, niter=15)
fe.power(d, n1, n2, p1, alpha = 0.05)
or2pcase(pcontrol, OR)response rate of standard treatment
response rate of experimental treatment
difference = p2-p1
control group probability
sample size for the standard treatment group
sample size for the standard treatment group
control group sample size
case group sample size
size of the test (default 5%)
power of the test (default 80%)
treatments are randomized in 1:r ratio (default r=1)
the sample size program searches for sample sizes in a range (+/- npm) to get the exact power
the maximum group size for which exact power is calculated
total number of subjects
proportion of patients who are marger positive
probability of response in all subjects
number of iterations in binary search
odds-ratio
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.
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).
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.