Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders

Sheng-Hsuan Lin*, Jessica G. Young, Roger Logan, Tyler J. VanderWeele

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The estimated overall 10-year all-cause mortality risk difference comparing “always smoke 30 cigarettes per day” versus “never smoke” was 4.3 (95% CI = (1.37, 6.30)). Of the overall effect, we estimated 7.91% (95% CI: = 1.36%, 19.32%) was mediated by the incidence and timing of coronary artery disease. The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data.

Original languageEnglish
Pages (from-to)4153-4166
Number of pages14
JournalStatistics in Medicine
Issue number26
StatePublished - 20 Nov 2017


  • longitudinal studies
  • mechanism investigation
  • mediation analysis
  • path analysis
  • survival
  • time varying


Dive into the research topics of 'Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders'. Together they form a unique fingerprint.

Cite this