Sample Size for Independent Cohort Studies

 

Menu location: Analysis_Sample Size_Independent Cohort.

 

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.