Summary measurements and screening in clinical trials with replicate observations

Repeating measurements of efficacy variables in clinical trials may be desirable when the measurement may be affected by ambient conditions. When such measurements are repeated at baseline and at the end of therapy, statistical questions relate to: (1) the best summary measurement to use for a subject when there is a possibility that some observations are contaminated and have increased variances; and (2) the effect of screening procedures which exclude outliers based on within- and between-subject contamination tests. We study these issues in two stages, each using a different set of models. The first stage deals only with the choice of the summary measure. The simulation results show that in some cases of contamination, the power achieved by the tests based on the median exceeds that achieved by the tests based on the mean of the replicates. However, even when we use the median, there are cases when contamination leads to a considerable loss in power. The combined issue of the best summary measurement and the effect of screening is studied in the second stage. The tests use either the observed data or the data after screening for outliers. The simulation results demonstrate that the power depends on the screening procedure as well as on the test statistic used in the study. We found that for the extent and magnitude of contamination considered, within-subject screening has a minimal effect on the power of the tests when there are at least three replicates; as a result, we found no advantage in the use of screening procedures for within-subject contamination. On the other hand, the use of a between-subject screening for outliers increases the power of the test procedures. However, even with the use of screening procedures, heterogeneity of variances can greatly reduce the power of the study.