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

Abstract

Existing methods for regression-based mediation analysis assume that the exposure-mediator effect, exposure-outcome effect, and mediator-outcome effect are constant across levels of the baseline characteristics of patients. However, investigators often have insight into how these underlying effects may be modified by baseline characteristics and are interested in how the resulting mediation effects, such as the natural direct effect (NDE), the natural indirect effect. (NIE), and the proportion mediated, are modified by these baseline characteristics. Motivated by an empirical example of anti-interleukin-1 therapy’s benefit on incident anemia reduction and its mediation by an early change in an inflammatory biomarker, we extended the closed-form regression-based causal mediation analysis with effect measure modification (EMM). Using a simulated numerical example, we demonstrated that naive analysis without considering EMM can give biased estimates of NDE and NIE and visually illustrated how baseline characteristics affect the presence and magnitude of EMM of NDE and NIE. We then applied the extended method to the empirical example informed by pathophysiologic insights into potential EMM by age, diabetes, and baseline inflammation. We found that the proportion modified through the early post-treatment inflammatory biomarker was greater for younger, nondiabetic patients with lower baseline level of inflammation, suggesting differential usefulness of the early post-treatment inflammatory biomarker in monitoring patients depending on baseline characteristics. To facilitate the adoption of EMM considerations in causal mediation analysis by the wider clinical and epidemiologic research communities, we developed a free- and open-source R package, regmedint.

Assessing the Effects of a Yearly Renewable Education Program Through Causal Mediation Analysis

“…The HSIS was conducted to evaluate the effectiveness of Head Start as a nation-wide early childhood education program by collecting a nationally representative data set of children and families who were eligible to enroll in Head Start programs with limited capacity (Puma et al, 2010a). 5 Then, natural direct and indirect effects were estimated within R (R Core Team, 2022) using the package regmedint (Li et al, 2023) to synthesize estimates and standard errors across 20 imputed data sets. 6…”