Abstract
Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019–1.2167) and 1.1388 (95% CI: 1.1339–1.1438), respectively.
Highlights
Mediation analysis was firstly used to deal with the causal chain of events as the primary exposure has an effect on the outcome through affecting one or more mediators in psychological studies, and gradually extended to sociological and biomedical researches (Baron and Kenny, 1986; MacKinnon et al, 2002; Preacher and Hayes, 2008; Biesanz et al, 2010; Huan et al, 2016)
Since the assumption of no confounders affecting the relation among exposure, mediator and outcome is violated in observational researches, we propose a new method using the propensity score as a covariate in the high-dimensional mediation model as follows, λi (t) = λ0 (t) exp γ ∗Xi + β1M1i + · · · βpMpi + φπi, (3)
The motivation of this study is that the assumption of no confounders affecting the relationship of exposure, mediators and outcome in the classical mediation model is difficult to be satisfied with observational studies
Summary
Mediation analysis was firstly used to deal with the causal chain of events as the primary exposure has an effect on the outcome through affecting one or more mediators in psychological studies, and gradually extended to sociological and biomedical researches (Baron and Kenny, 1986; MacKinnon et al, 2002; Preacher and Hayes, 2008; Biesanz et al, 2010; Huan et al, 2016). High-Dimensional Mediation Analysis With Confounders has been made in extensive of mediation methods to survival models (Lange and Hansen, 2011; VanderWeele, 2011; Wang and Zhang, 2011; Huang and Yang, 2017). As an observational (or non-randomized) study, it is unrealistic for a subject to be randomly assigned to the exposure, as moral and ethical factors, in the research of how smoking affects the lung cancer patients’ risk of progression to death mediated by DNA methylations. We study mediator selection and indirect effect estimation via high-dimensional mediation analysis in survival models with confounders. As the exposure is not randomly assigned, we propose to use the propensity score approach to adjust confounding effects. We conclude the paper through discussing limitations and other feasibilities
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