Dropouts in longitudinal studies: definitions and models.

The widely used distinction of Little and Rubin (1) about types of randomness for missing data presents difficulties in its application to dropouts in longitudinal repeated measurement studies. In its place, a new typology of randomness for dropouts is proposed that relies on using a survival model for the dropout process. In terms of a stochastic process, dropping out is a change of state. Then, the longitudinal measures and dropout processes can be modeled simultaneously, each conditional on the complete previous history of both repeated measures and states. In this context, Poisson regression is used to fit various proportional hazards models, some of which are new, to the dropout process using the longitudinal measurements responses as time-varying covariates. As examples of longitudinal measurement studies displaying nonrandom dropout processes, a dental study of testosterone production in rats and clinical trials for treatment of gallstones and of depression are analyzed.