Causal Mediation Analysis with Multiple Time-varying Mediators

Abstract

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, http://links.lww.com/EDE/B988.

A brief primer on conducting regression-based causal mediation analysis.

“…For multiple mediators, the joint effect of the set of mediators can be estimated using the causal mediation method (Valeri & VanderWeele, 2013, 2015) and easily obtained by using R package CMAverse (Shi et al, 2021). Recent work has established methods for decomposing certain pathway-specific effects (Tai et al, 2021).…”

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis

“…However, the natural direct and indirect effects redefined under a randomized interventional analogue are different from the classical NDE and NIE. 10 Although there is continued interest in developing mediation methods in the natural effects framework in recent years, [27][28][29][30][31][32][33][34][35] some have also criticized the use of natural effects because the cross-world independency assumptions do not have corresponding real-world implications, [36][37][38][39][40][41][42] though these views are not universal. 7,43 A new approach called "separable treatment effect approach" (or "interventionist approach") has been proposed that could mitigate the concerns around implausible cross-world assumptions by decomposing the treatment to two components to make the natural direct and indirect paths have an empirical meaning from the interventional perspective in the real world.…”

Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders: A method for generalized causal multi-mediation analysis

“…A few methods have recently been proposed for this complicated setting. 37,38 The article is organized as follows. Section 2 introduces notation and definitions.…”

“…(HBV at exam 1, HBV at exam 2, …, HBV at exam T), (GPT at exam 1, GPT at exam 2, …, GPT at exam T), (HCC at exam 1, HCC at exam 2, …, HCC at exam T)) as “multiple time-varying mediators.” A few methods have recently been proposed for this complicated setting. 37,38…”