A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies |
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| Authors: | Jung Yeon Park Melanie M. Wall Irini Moustaki Arnold H. Grossman |
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| Affiliation: | aDivision of Educational Psychology and Research Methods, George Mason University, Fairfax, VA, USA;bDepartment of Biostatistics and Psychiatry, Columbia University, New York, NY, USA;cNew York State Psychiatric Institute, New York, NY, USA;dDepartment of Statistics, London School of Economics and Political Science, London, UK;eDepartment of Applied Psychology, New York University, New York, NY, USA |
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| Abstract: | The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health |
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