A Bayesian semiparametric latent variable approach to causal mediation

Chanmin Kim, Michael Daniels, Yisheng Li, Kathrin Milbury, Lorenzo Cohen

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

In assessing causal mediation effects in randomized studies, a challenge is that the direct and indirect effects can vary across participants due to different measured and unmeasured characteristics. In that case, the population effect estimated from standard approaches implicitly averages over and does not estimate the heterogeneous direct and indirect effects. We propose a Bayesian semiparametric method to estimate heterogeneous direct and indirect effects via clusters, where the clusters are formed by both individual covariate profiles and individual effects due to unmeasured characteristics. These cluster-specific direct and indirect effects can be estimated through a set of regression models where specific coefficients are clustered by a stick-breaking prior. To let clustering be appropriately informed by individual direct and indirect effects, we specify a data-dependent prior. We conduct simulation studies to assess performance of the proposed method compared to other methods. We use this approach to estimate heterogeneous causal direct and indirect effects of an expressive writing intervention for patients with renal cell carcinoma.

Original languageEnglish (US)
Pages (from-to)1149-1161
Number of pages13
JournalStatistics in Medicine
Volume37
Issue number7
DOIs
StatePublished - Mar 30 2018

Keywords

  • Bayesian nonparametrics
  • causal inference
  • cluster-specific effects
  • effect modification
  • sequential ignorability

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

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