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Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data

Author

Listed:
  • Jing Huang

    (The University of Pennsylvania)

  • Ying Yuan

    (The University of Texas MD Anderson Cancer Center)

  • David Wetter

    (The University of Utah)

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.

Suggested Citation

  • Jing Huang & Ying Yuan & David Wetter, 2019. "Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 1-18, March.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:1:d:10.1007_s11336-018-09653-2
    DOI: 10.1007/s11336-018-09653-2
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    References listed on IDEAS

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