Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data

Jing Huang, Ying Yuan () and David Wetter
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Jing Huang: The University of Pennsylvania
Ying Yuan: The University of Texas MD Anderson Cancer Center
David Wetter: The University of Utah

Psychometrika, 2019, vol. 84, issue 1, No 1, 18 pages

Abstract: 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 inference; dynamic mediation; latent class; time-varying coefficients (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-018-09653-2

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