Adjusting for drop-out in clinical trials with repeated measures: Design and analysis issues

Recently, Wu and Follmann developed summary measures to adjust for informative drop-out in longitudinal studies where drop-out depends on the underlying tree value of the response. In this paper we evaluate these procedures in the common situation where drop-out depends on the observed responses. We also discuss various design and analysis strategies which minimize the bias obtained with this type of drop-out. Of particular interest is the use of multiple measurements of the response at each visit to reduce bias. These strategies are evaluated with a simulation study. The results are highlighted with applications to both a hypertensive and a respiratory disease clinical trial, where multiple measurements of the primary response were made for all participants at each visit.