Sample Size for Independent Cohort Studies

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This function gives the minimum number of case subjects required to detect a true relative risk or experimental event rate with power POWER and two sided type I error probability ALPHA. This sample size is also given as a continuity-corrected value intended for use with corrected chi-square and Fisher's exact tests (Casagrande et al. 1978; Meinert 1986; Fleiss, 1981; Dupont, 1990).

Information required

POWER: probability of detecting a real effect.
ALPHA: probability of detecting a false effect (two sided: double this if you need one sided).
P0: probability of event in controls.
*: input either P1 or RR, where RR=P1/P0.
P1: probability of event in experimental subjects.
RR: relative risk of events between experimental subjects and controls.
M: number of control subjects per experimental subject.

Practical issues

Usual values for POWER are 80%, 85% and 90%; try several in order to explore/scope.
5% is the usual choice for ALPHA.
P0 can be estimated as the population prevalence of the event under investigation.
If possible, choose a range of relative risks that you want have the statistical power to detect.

Technical validation

The estimated sample size n is calculated as:

- where α = alpha, β = 1 - power, n_c is the continuity corrected sample size and z_p is the standard normal deviate for probability p. n is rounded up to the closest integer.