2026-05-20T22:57:00Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/190022024-09-27T16:16:00Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608

00925njm 22002777a 4500 dc Domingo-Relloso, Arce author Jerolon, Allan author Tellez-Plaza, Maria author Bermudez, Jose D author 2024-02-18 Objective: The study of the potential intermediate effect of several variables on the association between an exposure and a time-to-event outcome is a question of interest in epidemiologic research. However, to our knowledge, no tools have been developed for the evaluation of multiple correlated mediators in a survival setting. Methods: In this work, we extended the multimediate algorithm, which conducts mediation analysis in the context of multiple uncausally correlated mediators, to a time-to-event setting using the semiparametric additive hazards model. We theoretically demonstrated that, under certain assumptions, indirect, direct and total effects can be calculated using the counterfactual framework with collapsible survival models. We also adapted the algorithm to accommodate exposure-mediator interactions. Results and conclusions: Using simulations, we demonstrated that our algorithm performs better than the product of coefficients method, even for uncorrelated mediators. The additive hazards model quantifies the effects as rate differences, which constitute a measure of impact, with applications that can be highly informative for public health. Our algorithm can be found in the R package multimediate, which is available in Github. medRxiv. 2024 Feb 18:2024.02.16.24302923. 10.1101/2024.02.16.24302923 medRxiv : the preprint server for health sciences 38405856 http://hdl.handle.net/20.500.12105/19002 Mediation analysis Survival analysis Correlated mediators Additive models Causal mediation for uncausally related mediators in the context of survival analysis