Marginal and association regression models for longitudinal binary data with drop-outs: a likelihood-based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likehhood-based approach to estimating both the mean response and association parameters for longitudinal binary data with drop-outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project. They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.