A Path‐Specific Effect Approach to Mediation Analysis With Time‐Varying Mediators and Time‐to‐Event Outcomes Accounting for Competing Risks

Year: 2026

Type: article

Abstract: Not accounting for competing events in survival analysis can lead to biased estimates, as individuals who die from other causes do not have the opportunity to develop the event of interest. Formal definitions and considerations for causal effects in the presence of competing risks have been published, but not for the mediation analysis setting when the exposure is not separable and both the outcome and the mediator are nonterminal events. We propose, for the first time, an approach based on the path-specific effects framework to account for competing risks in longitudinal mediation analysis with time-to-event outcomes. We do so by considering the pathway through the competing event as another mediator, which is nested within our longitudinal mediator of interest. We provide a theoretical formulation and related definitions of the effects of interest based on the mediational g-formula, as well as a detailed description of the algorithm. We also present a simulation study and an application of our algorithm to data from the Strong Heart Study, a prospective cohort of American Indian adults. In this application, we evaluated the mediating role of the blood pressure trajectory (measured in three visits) on the association of arsenic and cadmium with time to cardiovascular disease, accounting for competing risks by death. Identifying the effects through different paths enables us to evaluate the impact of metals on the outcome of interest, as well as through competing risks, more transparently.

Source: Statistics in Medicine

DOI: 10.1002/sim.70425

Authors: Arce Domingo‐Relloso, Yuchen Zhang, Ziqing Wang, Astrid M. Suchy‐Dicey, Dedra S. Buchwald, Ana Navas‐Acien, Joel Schwartz, Kiros Berhane, Brent A. Coull, Linda Valeri

Institutions: IE University, Columbia University, Boston University, University of Washington, Harvard University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,450

Funders: Strong, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Environmental Health Sciences

OpenAlex ID: W7127901932