Mediation analysis for count and zero-inflated count data

Jing Cheng, Nancy F. Cheng, Zijian Guo, Steven Gregorich, Amid I. Ismail, Stuart A. Gansky

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Different conventional and causal approaches have been proposed for mediation analysis to better understand the mechanism of a treatment. Count and zero-inflated count data occur in biomedicine, economics, and social sciences. This paper considers mediation analysis for count and zero-inflated count data under the potential outcome framework with nonlinear models. When there are post-treatment confounders which are independent of, or affected by, the treatment, we first define the direct, indirect, and total effects of our interest and then discuss various conditions under which the effects of interest can be identified. Proofs are provided for the sensitivity analysis proposed in the paper. Simulation studies show that the methods work well. We apply the methods to the Detroit Dental Health Project’s Motivational Interviewing DVD trial for the direct and indirect effects of motivational interviewing on count and zero-inflated count dental caries outcomes.

Original languageEnglish (US)
Pages (from-to)2756-2774
Number of pages19
JournalStatistical Methods in Medical Research
Volume27
Issue number9
DOIs
StatePublished - Sep 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Keywords

  • Direct effect
  • indirect effect
  • post-treatment confounder
  • sensitivity analysis
  • sequential ignorability

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