Causal models for mediation analysis: An introduction to structural mean models

Cheng Zheng, David C. Atkins, Xiao Hua Zhou, Isaac C. Rhew

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Mediation analyses are critical to understanding why behavioral interventions work. To yield a causal interpretation, common mediation approaches must make an assumption of “sequential ignorability.” The current article describes an alternative approach to causal mediation called structural mean models (SMMs). A specific SMM called a rank-preserving model (RPM) is introduced in the context of an applied example. Particular attention is given to the assumptions of both approaches to mediation. Applying both mediation approaches to the college student drinking data yield notable differences in the magnitude of effects. Simulated examples reveal instances in which the traditional approach can yield strongly biased results, whereas the RPM approach remains unbiased in these cases. At the same time, the RPM approach has its own assumptions that must be met for correct inference, such as the existence of a covariate that strongly moderates the effect of the intervention on the mediator and no unmeasured confounders that also serve as a moderator of the effect of the intervention or the mediator on the outcome. The RPM approach to mediation offers an alternative way to perform mediation analysis when there may be unmeasured confounders.

Original languageEnglish (US)
Pages (from-to)614-631
Number of pages18
JournalMultivariate Behavioral Research
Volume50
Issue number6
DOIs
StatePublished - Jan 1 2015

Keywords

  • Clinical trials
  • Mediation analysis
  • Structural mean models

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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