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.
Original language | English (US) |
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Pages (from-to) | 1-18 |
Number of pages | 18 |
Journal | Psychometrika |
Volume | 84 |
Issue number | 1 |
DOIs | |
State | Published - Mar 15 2019 |
Keywords
- Bayesian inference
- dynamic mediation
- latent class
- time-varying coefficients
ASJC Scopus subject areas
- General Psychology
- Applied Mathematics
MD Anderson CCSG core facilities
- Biostatistics Resource Group