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Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data

Published online by Cambridge University Press:  01 January 2025

Jing Huang ,
Ying Yuan  and
David Wetter
Jing Huang
Affiliation:
The University of Pennsylvania
Ying Yuan*
Affiliation:
The University of Texas MD Anderson Cancer Center
David Wetter
Affiliation:
The University of Utah
*
Correspondence should be made to Ying Yuan, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Email: yyuan@mdanderson.org
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Abstract

Traditional mediation analysis assumes that a study population is homogeneous and the mediation effect is constant over time, which may not hold in some applications. Motivated by smoking cessation data, we propose a latent class dynamic mediation model that explicitly accounts for the fact that the study population may consist of different subgroups and the mediation effect may vary over time. We use a proportional odds model to accommodate the subject heterogeneities and identify latent subgroups. Conditional on the subgroups, we employ a Bayesian hierarchical nonparametric time-varying coefficient model to capture the time-varying mediation process, while allowing each subgroup to have its individual dynamic mediation process. A simulation study shows that the proposed method has good performance in estimating the mediation effect. We illustrate the proposed methodology by applying it to analyze smoking cessation data.

Keywords

Bayesian inferencedynamic mediationlatent classtime-varying coefficients
Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 1 , 15 March 2019 , pp. 1 - 18
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-09653-2) contains supplementary material, which is available to authorized users.

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