The Practical Use of Different Strategies to Handle Dropout in Longitudinal Studies

Abstract

In the presence of dropout, valid statistical inferences based on longitudinal data can, in general, only be obtained from modeling the measurement process and the dropout process simultaneously. Many models have been proposed in the statistical literature, most of which have been formulated within the framework of selection models or pattern-mixture models. In this paper, we will use continuous data from a longitudinal clinical trial with a 24% dropout rate to illustrate some of the models frequently used in practice. We emphasize the underlying implicit assumptions made by the different approaches, and the sensitivity of the results with respect to these assumptions. The merits and drawbacks of the procedures are extensively discussed and compared from a practical point of view.