A joint modeling approach for longitudinal outcomes and non-ignorable dropout under population heterogeneity in mental health studies

Park, Jung Yeon, Wall, Melanie M, Moustaki, Irini ORCID: 0000-0001-8371-1251 and Grossman, Arnold (2022) A joint modeling approach for longitudinal outcomes and non-ignorable dropout under population heterogeneity in mental health studies. Journal of Applied Statistics, 49 (13). pp. 3361-3376. ISSN 0266-4763

Text (JointModel_DropoutMissing_MentalHealth_AcceptedVersion_with_Figures_and_Tables) - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (596kB)

• Author

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.

Item Type:	Article
Official URL:	https://www.tandfonline.com/toc/cjas20/current
Additional Information:	© 2021 Informa UK Limited, trading as Taylor & Francis Group
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	16 Jun 2021 09:06
Last Modified:	12 Dec 2024 02:33
URI:	http://eprints.lse.ac.uk/id/eprint/110867

Actions (login required)

View Item