A sensitivity analysis for dropout mechanism in longitudinal data using reversible jump MCMC

Existence of missing values is an inseparable part of longitudinal studies in epidemi- ology, medical and clinical studies. Usually researchers, for simplicity, ignore the missingness mechanism while, ignoring a not at random mechanism may lead to misleading results. In this paper, we use a Bayesian paradigm for fitting selection model of Heckman (Heckman, 1976), which allows the non-ignorable missingness for longitudinal data. Also, We use reversible-jump Markov chain Monte Carlo to allow the model to choose between non-ignorable and ignorable structures for missingness mechanism, and show how the selection can be incorporated. The approach is also used for analyzing a real data set