Journal article
High-dimensional causal mediation analysis based on partial linear structural equation models
Computational statistics & data analysis, Vol.174, p.107501
10/2022
DOI: 10.1016/j.csda.2022.107501
Abstract
Causal mediation analysis has become popular in recent years. The goal of mediation analyses is to learn the direct effects of exposure on outcome as well as mediated effects on the pathway from exposure to outcome. A set of generalized structural equations to estimate the direct and indirect effects for mediation analysis is proposed when the number of mediators is of high-dimensionality. Specifically, a two-step procedure is considered where the penalization framework can be adopted to perform variable selection. A partial linear model is used to account for a nonlinear relationship among pre-treatment confounders and the response variable in each model. Procedures for estimating the coefficients for the treatment and the mediators in the structural models are developed. The obtained estimators can be interpreted as causal effects without imposing a linear assumption on the model structure. The performance of Sobel's method in obtaining the standard error and confidence interval for the estimated joint indirect effect is also evaluated in simulation studies. Simulation results show a superior performance of the proposed method. It is applied to an epidemiologic study in which the goal is to understand how DNA methylation mediates the effect of childhood trauma on regulation of human stress reactivity.
Details
- Title: Subtitle
- High-dimensional causal mediation analysis based on partial linear structural equation models
- Creators
- Xizhen Cai - Williams CollegeYeying Zhu - University of WaterlooYuan Huang - Department of Biostatistics, Yale School of Public Health, New Haven, CT 06511, United StatesDebashis Ghosh - Colorado School of Public Health
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.174, p.107501
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.csda.2022.107501
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Grant note
- DOI: 10.13039/100000086, name: National Science Foundation Directorate for Mathematical and Physical Sciences; DOI: 10.13039/501100000038, name: Natural Sciences and Engineering Research Council of Canada
- Language
- English
- Date published
- 10/2022
- Academic Unit
- Biostatistics
- Record Identifier
- 9984363668602771
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