Measurement error correction in mediation analysis under the additive hazards model

Abstract

In the disciplines of medicine, biology and social science, causal mediation analysis is widely used to examine the effect of an exposure on an outcome through different causal pathways. Recently, there is rapid development of mediation analysis with survival data. An early work of Lange and Hansen proposed to identify the direct and indirect causal effects under the additive hazards model with a single mediator. However, inaccurate measurements of mediators and confounders may lead to biased causal effect estimation. We propose a measurement error correction approach to tackle measurement error in the mediators and confounders under the additive hazards model where multiple mediators are present. We apply the proposed corrected method to the ACTG175 Study data set, and uncover interesting findings.

Keywords:

• Causal inference
• Direct effect
• Indirect effect
• Measurement error
• Survival analysis

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Funding

Ying Yan’s research is partly supported by a grant from National Natural Science Foundation of China (NSFC) (NO.11901599). Alexander de Leon’s research is partly supported by The Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2018-04748).