Department of Gynecology and Obstetrics, Emory University, Atlanta, Georgia, USA.
Department of Human Genetics, Emory University, Atlanta, Georgia, USA.
Stat Med. 2022 Jul 10;41(15):2879-2893. doi: 10.1002/sim.9390. Epub 2022 Mar 30.
Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.
中介模型是一组统计技术，用于研究暴露变量和结果变量之间观察到的关系产生的机制，以推断该关系在多大程度上受到中间中介变量的影响。对于病例对照研究，最常用的中介分析策略采用反事实框架，该框架允许在二分或连续中介的情况下，对二项结局的比值比尺度进行间接和直接效应的估计。虽然这个框架已经成为中介分析的重要工具，但我们证明，我们可以将这种方法嵌入到病例对照研究中的统一似然框架中，以利用数据的更多特征（特别是暴露和中介之间的关系）来提高间接效应估计的效率。我们似然方法的一个重要特点是，它可以自然地将病例纳入暴露-中介模型中，以提高效率。我们的方法不需要疾病流行率的知识，可以对混杂因素和暴露-中介相互作用进行建模，并且可以在标准统计软件中轻松实现。我们使用模拟数据和肺癌病例对照遗传研究的真实数据来说明我们的方法。
