Sample size estimation using repeated measurements on biomarkers as outcomes.

The objectives of this paper are to (1) examine methods of using longitudinal data in designing comparative trials and calculating sample sizes or power and (2) show the effect of autocorrelation of repeated measures on the assessment of sample sizes. A statistical model with a simple regression structure for the mean trajectory of the longitudinal data and a two-parameter model for the correlations of within-individual observations given by corr(yt,yt+s) = gamma s theta is used. The methods are illustrated by considering a two-group trial and investigating the effect of different values of the correlation parameters, gamma and theta on the sample size. The results show that taking account of the autocorrelation structure of longitudinal data may lead to more efficient designs. Specifically, the stronger the autocorrelation is, the smaller the sample size that is required.