Volume 36 • Number 3 • 2013   • A Multistate Transition Model for Analyzing Longitudinal Depression Data M. Ataharul Islam, Rafiqul I. Chowdhury and Shahariar Huda Abstract. In longitudinal data analysis, there are many practical situations where we need to deal with transitions to a number of states and which are repeated over time generating a large number of trajectories from beginning to end of the study. This problem becomes increasingly difficult to model if the number of follow-ups is increased for a set of longitudinal data. A covariate-dependent Markov transition model is proposed using the logistic link function for polytomous outcome data. A generalized and more flexible approach of constructing the likelihood function for the first or higher order is demonstrated in this paper to deal with the branching of a number of transition types starting from no depression at the beginning of the study. The proposed method can be employed to resolve a longstanding problem in dealing with modeling for transitions, reverse transitions and repeated transitions by reducing the number of trajectories to a large extent resulting in estimating relatively few parameters. The problem of depression in elderly, in terms of short and long-term health and economic consequence, needs to be assessed more critically. This study uses the longitudinal data from the six waves of the Health and Retirement Survey to examine the transition to depression, reverse transition from depression to no depression and also repeated transition from no depression to depression after experiencing a reverse transition during a study period. The results indicate that age is negatively associated with reverse and repeated transitions, gender is negatively associated with transition and reverse transition indicating that females are more likely to experience both. The proposed method clearly provides a wider range of useful information in revealing the dynamics of the depression pattern among elderly. 2010 Mathematics Subject Classification: 62H20, 62F03, 62F10, 62N03 Full text: PDF