Finite Mixtures of Hidden Markov Models for Longitudinal Responses Subject to Drop out

Abstract

Drop out is a typical issue in longitudinal studies. When the missingness is non-ignorable, inference based on the observed data only may be biased. This paper is motivated by the Leiden 85+ study, a longitudinal study conducted to analyze the dynamics of cognitive functioning in the elderly. We account for dependence between longitudinal responses from the same subject using time-varying random effects associated with a heterogeneous hidden Markov chain. As several participants in the study drop out prematurely, we introduce a further random effect model to describe the missing data mechanism. The potential dependence between the random effects in the two equations (and, therefore, between the two processes) is introduced through a joint distribution specified via a latent structure approach. The application of the proposal to data from the Leiden 85+ study shows its effectiveness in modeling heterogeneous longitudinal patterns, possibly influenced by the missing data process. Results from a sensitivity analysis show the robustness of the estimates with respect to misspecification of the missing data mechanism. A simulation study provides evidence for the reliability of the inferential conclusions drawn from the analysis of the Leiden 85+ data.     

Die akute Therapie des ischämischen Schlaganfalles

Seit Etablierung unserer ersten Stroke Units als Spezialstationen zur Akutbehandlung von Schlaganfallpatienten 1996 und 1998 sind die Herausforderungen an das Management im Bereich der akuten Schlaganfalltherapie durch steigende Patientenzahlen, zunehmende Lyseraten und abnehmende Liegedauer in Kombination mit neuen, Erfolg versprechenden Therapieoptionen stetig angestiegen.