Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data.

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 p

SUBMITTER: Huang J 

PROVIDER: S-EPMC6594758 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature