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A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

Author

Listed:
  • Jung Yeon Park
  • Melanie M. Wall
  • Irini Moustaki
  • Arnold H. Grossman

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.

Suggested Citation

  • Jung Yeon Park & Melanie M. Wall & Irini Moustaki & Arnold H. Grossman, 2022. "A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(13), pages 3361-3376, October.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3361-3376
    DOI: 10.1080/02664763.2021.1945000
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