Causal Mediation Analysis with Multiple Time-varying Mediators

An Shun Tai, Sheng Hsuan Lin*, Yu Cheng Chu, Tsung Yu, Milo A. Puhan, Tyler VanderWeele

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at,

Original languageEnglish
Pages (from-to)8-19
Number of pages12
Issue number1
StatePublished - 1 Jan 2023


  • Causal inference
  • COPD
  • Mediation analysis
  • Multiple mediators
  • Time-varying confounder
  • Time-varying mediator


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