Bayesian Inference for the Causal Effect of Mediation

被引：35

作者：

Daniels, Michael J. ^{[1
]}

Roy, Jason A. ^{[2
]}

Kim, Chanmin ^{[1
]}

Hogan, Joseph W. ^{[3
]}

Perri, Michael G. ^{[4
]}

机构：

[1] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

[2] Univ Penn, Dept Biostat, Philadelphia, PA 19104 USA

[3] Brown Univ, Dept Biostat, Providence, RI 02912 USA

[4] Univ Florida, Dept Clin & Hlth Psychol, Gainesville, FL 32611 USA

来源：

BIOMETRICS | 2012年 / 68卷 / 04期

关键词：

Causal inference; Direct effect; Indirect effect; Mediators; Nonparametric Bayes; Sensitivity analysis; PRINCIPAL STRATIFICATION; SENSITIVITY-ANALYSIS; IDENTIFIABILITY;

D O I：

10.1111/j.1541-0420.2012.01781.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess mediation in a recent weight management clinical trial.

引用

页码：1028 / 1036

页数：9

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