Causal mediation analysis in the context of clinical research

Zhongheng Zhang, Cheng Zheng, Chanmin Kim, Sven Van Poucke, Su Lin, Peng Lan

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Clinical researches usually collected numerous intermediate variables besides treatment and outcome. These variables are often incorrectly treated as confounding factors and are thus controlled using a variety of multivariable regression models depending on the types of outcome variable. However, these methods fail to disentangle underlying mediating processes. Causal mediation analysis (CMA) is a method to dissect total effect of a treatment into direct and indirect effect. The indirect effect is transmitted via mediator to the outcome. The mediation package is designed to perform CMA under the assumption of sequential ignorability. It reports average causal mediation effect (ACME), average direct effect (ADE) and total effect. Also, the package provides visualization tool for these estimated effects. Sensitivity analysis is designed to examine whether the results are robust to the violation of the sequential ignorability assumption since the assumption has been criticized to be too strong to be satisfied in research practice.

Original languageEnglish (US)
Article number425
JournalAnnals of Translational Medicine
Issue number21
StatePublished - Nov 2016
Externally publishedYes


  • Causal mediation analysis (CMA)
  • Clinical research
  • Mediator
  • Sensitivity analysis

ASJC Scopus subject areas

  • Medicine(all)

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