Keywords: Ecological Momentary Assessments, Informative Missing, Mixed Effects, Shared Parameter Model
In this paper, we address the problem of informative missing in the context of Ecological Momentary Assessment studies (sometimes referred to as intensive longitudinal studies), where each study unit gets measured intensively over time and intermittent missing are usually present. We present a shared parameter model approach that links the primary longitudinal outcome with potentially informative missingness by a common set of random effects that summarizes subjects' specific traits in terms of their mean (location) and variability (scale). The primary outcome, conditional on the random effects, are allowed to exhibit heterogeneity with respect to both the mean and variance, and are further assumed to be independent of the missing mechanism.Unlike the previous methods which largely rely on numerical integration or approximation, we estimate the model by a full Bayesian approach using Markov Chain Monte Carlo. An adolescent mood study example is illustrated together with a series of simulation studies. Results in comparison to the naive approaches suggest that accounting for the common but unobserved variables in mean and variance can dramatically increase the model fit.