Yu Qingzhao, Cao Wentao, Mercante Donald, Wu Xiaocheng, Li Bin
Biostatistics, LSU Health-New Orleans, New Orleans, LA 70112, USA.
Louisiana Tumor Registry, LSU Health-New Orleans, New Orleans, LA 70112, USA.
Behaviormetrika. 2023 Jan;50(1):361-383. doi: 10.1007/s41237-022-00185-9. Epub 2022 Oct 15.
Third-variables refer to the middle variables that are positioned in the pathway between an exposure and an outcome variable. Mediation analysis is a statistical approach to identify third variables, and to estimate and test third-variable effects that explain the exposure - outcome association. In this paper, we propose three methods for mediation analysis in Bayesian settings: (1) the function of coefficients method, (2) the product of partial differences method, and (3) the resampling method. The explicit benefit of the Bayesian mediation analysis is that the hierarchical relationships between the exposure variable and third variables, and between third variables and the outcome are naturally built into the Bayesian models. We performed sensitivity analysis to assess the impact of the choice of prior distributions in the three Bayesian inference methods. We found that the proposed methods are robust across a range of priors. Finally, we illustrate the proposed methods using real data from the MY-Health Study to explore racial/ethnic disparities in anxiety among cancer survivors. The results are comparable to those from the Frequentist's general mediation analysis but request shorter computing time.
第三变量是指位于暴露变量和结果变量之间路径上的中间变量。中介分析是一种识别第三变量、估计和检验解释暴露-结果关联的第三变量效应的统计方法。在本文中，我们提出了三种在贝叶斯环境下进行中介分析的方法：（1）系数函数法，（2）偏差异乘积法，以及（3）重采样法。贝叶斯中介分析的显著优势在于，暴露变量与第三变量之间以及第三变量与结果之间的层次关系自然地构建到了贝叶斯模型中。我们进行了敏感性分析，以评估三种贝叶斯推断方法中先验分布选择的影响。我们发现，所提出的方法在一系列先验条件下都具有稳健性。最后，我们使用来自MY健康研究的真实数据来说明所提出的方法，以探讨癌症幸存者焦虑方面的种族/民族差异。结果与频率学派的一般中介分析结果相当，但计算时间更短。
