Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data |
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Authors: | Huang Jing Yuan Ying Wetter David |
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Affiliation: | 1.Department of Biostatistics, Epidemiology and Informatics, The University of Pennsylvania, Philadelphia, PA, USA ;2.Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA ;3.Huntsman Center for HOPE and the Department of Population Health Sciences, The University of Utah, Salt Lake City, UT, USA ; |
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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. |
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