International Biometric Society Stochastic EM algorithm for incomplete longitudinal data

Missingness often occurs in data arising from longitudinal studies, inducing imbalance in the sense that not all planned observations are actually made. More specifically, missingness in longitudinal studies usually appears in the form of dropouts, in which subjects fail to complete the study for some reason or another. In his 1976 paper, Rubin provided a formal framework for the field of incomplete data by introducing the important taxonomy of missing data mech-anisms, consisting of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). A missingness process is said to be MCAR if the missingness is independent of both unobserved and observed outcomes, but potentially depends on co-variates. An MAR mechanism depends on the observed outcomes and perhaps also on the covariates, but not further on unobserved measurements. Finally, when an MNAR mechanism is operating, missingness does depend on unobserved measurements, maybe in addition to dependencies on covariates and/or on observed outcomes. At the same time, the selection model, the pattern-mixture model, and the shared-parameter model frameworks have been established. In this talk we focus on full selection models to deal with incomplete longitudinal continuou