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

Recently, Wu and Follmann developed summary measures to adjust for informat ive drop-out in longitudinal studies where drop-out depends on the underlyi ng true value of the response. In this paper we evaluate these procedures i n the common situation where drop-out depends on the observed responses. We also discuss various design and analysis strategies which minimize the bia s 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 highlight ed with applications to both a hypertensive and a respiratory disease clini cal trial, where multiple measurements of the primary response were made fo r all participants at each visit. Copyright (C) 2001 John Wiley & Sons, Ltd .