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

DOI10.1080/02664763.2021.1945000OpenAlexW3171312167MaRDI QIDQ5044650

Jung Yeon Park, Arnold H. Grossman, Irini Moustaki, Melanie M. Wall

Publication date: 2 November 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2021.1945000

zbMATH Keywords

survival analysisfinite mixture modelmental healthlatent growth curveMNAR drop-out

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• Mplus
• Stan

Cites Work

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• Understanding predictive information criteria for Bayesian models
• High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature
• A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
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• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• Shared parameter models under random effects misspecification
• Inference and missing data
• Informative Drop-Out in Longitudinal Data Analysis
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution
• A Latent‐Class Mixture Model for Incomplete Longitudinal Gaussian Data
• Latent variable models for mixed categorical and survival responses, with an application to fertility preferences and family planning in Bangladesh
• Linear mixed models for longitudinal data

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