Sample size calculation for time-averaged differences in the presence of missing data

Sample size calculations based on two-sample comparisons of slopes in repeated measurements have been reported by many investigators. In contrast, the literature has paid relatively little attention to the sample size calculations for time-averaged differences in the presence of missing data in repeated measurement studies. Diggle et al. (2002) provided a sample size formula detecting time-averaged differences for continuous outcomes in repeated measurement studies assuming no missing data and the compound symmetry (CS) correlation structure among outcomes from the same subject. In this paper we extend Diggle et al.'s timeaveraged difference sample size formula by allowing missing data and various correlation structures. We propose to use the generalized estimating equation (GEE) method to compare the time-averaged differences in repeatedmeasurement studies and introduce a closed form formula for sample size and power. Simulation studies were conducted to investigate the performance of GEE sample size formula with small sample sizes, a damped exponential family of correlation structures and missing data. The proposed sample size formula is illustrated using a clinical trial example.