Causal mediation analysis with latent subgroups

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • WenWu Wang
  • Jinfeng Xu
  • Joel Schwartz
  • Andrea Baccarelli
  • Zhonghua Liu


Original languageEnglish
Pages (from-to)5628-5641
Journal / PublicationStatistics in Medicine
Issue number25
Online published15 Jul 2021
Publication statusPublished - 10 Nov 2021
Externally publishedYes


In biomedical studies, the causal mediation effect might be heterogeneous across individuals in the study population due to each study subject's unique characteristics. While individuals' mediation effects may differ from each other, it is often reasonable and more interpretable to assume that individuals belong to several distinct latent subgroups with similar attributes. In this article, we first show that the subgroup-specific mediation effect can be identified under the group-specific sequential ignorability assumptions. Then, we propose a simple mixture modeling approach to account for the latent subgroup structure where each mixture component corresponds to one latent subgroup in the linear structural equation model framework. Model parameters can be estimated using the standard expectation-maximization (EM) algorithm. Each individual's subgroup membership can be inferred based on the posterior probability. We propose to use the singular Bayesian information criterion to consistently select the number of latent subgroups by recognizing that the Fisher information matrix for mixture models might be singular. We then propose to use nonparametric bootstrap method to compute standard errors and confidence intervals. We conducted simulation studies to evaluate the empirical performance of our proposed method named iMed. Finally, we reanalyzed a DNA methylation data set from the Normative Aging Study and found that the mediation effects of two well-documented DNA methylation CpG sites are heterogeneous across two latent subgroups in the causal pathway from smoking behavior to lung function. We also developed an R package iMed for public use.

Research Area(s)

  • DNA methylation, EM algorithm, heterogeneous mediation effects, latent subgroups, mixture model, singular Bayesian information criterion

Citation Format(s)

Causal mediation analysis with latent subgroups. / Wang, WenWu; Xu, Jinfeng; Schwartz, Joel et al.

In: Statistics in Medicine, Vol. 40, No. 25, 10.11.2021, p. 5628-5641.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review