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Causal mediation analysis of survival outcome with multiple mediators.

Causal mediation analysis of survival outcome with multiple mediators.
Author Information (click to view)

Huang YT, Yang HI,


Huang YT, Yang HI, (click to view)

Huang YT, Yang HI,

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Epidemiology (Cambridge, Mass.) 2017 03 14() doi 10.1097/EDE.0000000000000651

Abstract
BACKGROUND
Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multi-mediator models for survival outcomes is still limited.

METHODS
We present methods for multi-mediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram.

RESULTS
Mediation analyses using Cox models under the rare outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time as well as survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load.

CONCLUSIONS
Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.

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