Kisbu-Sakarya Yasemin, MacKinnon David P, Valente Matthew J, Çetinkaya Esra
Department of Psychology, Koç University, Istanbul, Turkey.
Department of Psychology, Arizona State University, Tempe, AZ, United States.
Front Psychol. 2020 Aug 14;11:2067. doi: 10.3389/fpsyg.2020.02067. eCollection 2020.
In many disciplines, mediating processes are usually investigated with randomized experiments and linear regression to determine if the treatment affects the outcome through a mediator. However, randomizing the treatment will not yield accurate causal direct and indirect estimates unless certain assumptions are satisfied since the mediator status is not randomized. This study describes methods to estimate causal direct and indirect effects and reports the results of a large Monte Carlo simulation study on the performance of the ordinary regression and modern causal mediation analysis methods, including a previously untested doubly robust sequential g-estimation method, when there are confounders of the mediator-to-outcome relation. Results show that failing to measure and incorporate potential post-treatment confounders in a mediation model leads to biased estimates, regardless of the analysis method used. Results emphasize the importance of measuring potential confounding variables and conducting sensitivity analysis.
在许多学科中，通常采用随机试验和线性回归来研究中介过程，以确定治疗是否通过中介影响结果。然而，除非满足某些假设，否则对治疗进行随机化不会产生准确的因果直接和间接估计，因为中介状态并未随机化。本研究描述了估计因果直接和间接效应的方法，并报告了一项大型蒙特卡洛模拟研究的结果，该研究考察了普通回归和现代因果中介分析方法（包括一种之前未经测试的双重稳健序贯g估计方法）在存在中介到结果关系的混杂因素时的性能。结果表明，在中介模型中未能测量和纳入潜在的治疗后混杂因素会导致估计有偏差，无论使用何种分析方法。结果强调了测量潜在混杂变量和进行敏感性分析的重要性。
