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Mediation analysis for a survival outcome with time‐varying exposures, mediators, and confounders
Author(s) -
Lin ShengHsuan,
Young Jessica G.,
Logan Roger,
VanderWeele Tyler J.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7426
Subject(s) - confounding , mediation , framingham heart study , medicine , coronary artery disease , survival analysis , proportional hazards model , covariate , incidence (geometry) , demography , disease , framingham risk score , statistics , mathematics , geometry , sociology , political science , law
We propose an approach to conduct mediation analysis for survival data with time‐varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g‐formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The estimated overall 10‐year all‐cause mortality risk difference comparing “always smoke 30 cigarettes per day” versus “never smoke” was 4.3 (95% CI = (1.37, 6.30)). Of the overall effect, we estimated 7.91% (95% CI: = 1.36%, 19.32%) was mediated by the incidence and timing of coronary artery disease. The survival mediational g‐formula constitutes a powerful tool for conducting mediation analysis with longitudinal data.