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Sample Size for Paired Cohort Studies
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This function gives you the minimum number of subject pairs that you require to detect a true relative risk RR with power POWER and two sided type I error probability ALPHA (Dupont, 1990; Breslow and Day, 1980).
- POWER: probability of detecting a real effect.
- ALPHA: probability of detecting a false effect (two sided: double this if you need one sided).
- r: correlation coefficient for failure between paired subjects.
- *: input either (P0 and RR) or (P0 and P1), where RR=P0/P1.
- P0: event rate in the control group.
- P1: event rate in experimental group.
- RR: risk of failure of experimental subjects relative to controls.
- Usual values for POWER are 80%, 85% and 90%; try several in order to explore/scope.
- 5% is the usual choice for ALPHA.
- r can be estimated from previous studies - note that r is the phi (correlation) coefficient that is given for a two by two table if you enter it into the StatsDirect r by c chi-square function. When r is not known from previous studies, some authors state that it is better to use a small arbitrary value for r, say 0.2, than it is to assume independence (a value of 0) (Dupont, 1988).
- P0 can be estimated as the population event rate. Note, however, that due to matching, the control sample is not a random sample from the population therefore population event rate can be a poor estimate of P0 (especially if confounders are strongly associated with the event).
- If possible, choose a range of relative risks that you want have the statistical power to detect.
The estimated sample size n is calculated as:
- where α = alpha, β = 1 - power and zp is the standard normal deviate for probability p. n is rounded up to the closest integer.
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