, Volume 84, Issue 1, pp 1–18 | Cite as

Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data

  • Jing Huang
  • Ying YuanEmail author
  • David Wetter


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.


Bayesian inference dynamic mediation latent class time-varying coefficients 


Supplementary material

11336_2018_9653_MOESM1_ESM.pdf (207 kb)
Supplementary material 1 (pdf 206 KB)


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Copyright information

© The Psychometric Society 2019

Authors and Affiliations

  1. 1.Department of Biostatistics, Epidemiology and InformaticsThe University of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  3. 3.Huntsman Center for HOPE and the Department of Population Health SciencesThe University of UtahSalt Lake CityUSA

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