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Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data


  • 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

    1. Michael J. Daniels & Jason A. Roy & Chanmin Kim & Joseph W. Hogan & Michael G. Perri, 2012. "Bayesian Inference for the Causal Effect of Mediation," Biometrics, The International Biometric Society, vol. 68(4), pages 1028-1036, December.
    2. Patrick J. Heagerty & Bryan A. Comstock, 2013. "Exploration of Lagged Associations using Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(1), pages 197-205, March.
    3. repec:aph:ajpbhl:1998:88:1:86-89_7 is not listed on IDEAS
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Sara Geneletti, 2007. "Identifying direct and indirect effects in a non‐counterfactual framework," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 199-215, April.
    6. Thomas R. Ten Have & Marshall M. Joffe & Kevin G. Lynch & Gregory K. Brown & Stephen A. Maisto & Aaron T. Beck, 2007. "Causal Mediation Analyses with Rank Preserving Models," Biometrics, The International Biometric Society, vol. 63(3), pages 926-934, September.
    7. Ten Have, Thomas R. & Elliott, Michael R. & Joffe, Marshall & Zanutto, Elaine & Datto, Catherine, 2004. "Causal Models for Randomized Physician Encouragement Trials in Treating Primary Care Depression," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 16-25, January.
    8. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
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