Statistical modelling and confounder selection for causal mediation analyses

因果中介分析的统计模型和混杂因素选择

• 批准号: RGPIN-2020-05473
• 负责人: Lefebvre, Geneviève
• 金额: $ 1.97万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717324
• 关键词: Statistical; modelling; confounder; selection; causal

The proposed research program concerns the development of statistical methods for causal mediation. Mediation analyses are an increasingly popular class of techniques used for estimating exposure (treatment) effects under the assumption that there is a mediating variable on the causal pathway between exposure and outcome. Such an analysis aims to decompose the total exposure effect in an indirect effect through the mediator and in a direct effect which avoids the mediation path. The program is divided in three themes that address relevant modelling issues in causal mediation: (1) developing an exact regression-based mediation approach for a binary outcome; (2) adapting two existing Bayesian regression models for purposes of causal mediation; (3) investigating confounder selection for valid estimation of mediation effects. Regarding (1), we have recently observed that the “rare outcome assumption” made in the conventional regression approach for a binary outcome can lead to wrong inferences in practice. We will propose an exact estimator for the natural direct and indirect effects whose theoretical validity does not rely on the rareness (or commonness) of the outcome. This will be accomplished for both binary and continuous mediators, where a logit-normal representation will be used for the nested counterfactual outcome probabilities in the latter case. For theme (2), we are planning to expand on Bayesian mediation methodology by building on two regression models with attractive features. First, we propose a latent multivariate Student-t model as a basis for an exact mediation approach for a binary outcome and possibly multiple binary mediators. Secondly, we aim to elaborate a flexible mediation strategy based on Bayesian additive regression trees (BART) models. The Mediation Formula will be used to express the natural mediation effects from both model specifications. There is currently a lack of information regarding how to select covariates for adjustment in causal mediation models when substantive knowledge is alone not sufficient for this task. In theme (3), we will target two well known confounder selection algorithms introduced for non-mediated contexts and investigate how they can be modified for mediation contexts in order to yield unbiased and efficient estimators of direct and indirect effects. All three themes will require theoretical developments and validation through simulation studies. Open-access software will be created and comprehensive examples will be made available to increase uptake by substantive researchers. To conclude, this program is about original and innovative statistical developments on several important aspects of causal mediation analyses. It is expected to increase the reliability of results obtained from this type of analyses in many fields of applied research (e.g., social science, epidemiology).

拟议的研究计划涉及开发灾难性调解的统计方法。调解分析是一种越来越流行的技术类别，用于估算暴露效果（治疗）效应，假设在暴露和结果之间的因果途径上存在介导变量。这样的分析旨在通过调解器和直接效应以避免调解路径的直接效应来分解总暴露效应。 该程序分为三个主题，这些主题解决了催化中介中相关的建模问题：（1）为二进制结果开发基于回归的确切调解方法； （2）为催化介导的目的调整两个现有的贝叶斯回归模型； （3）调查混杂因子选择中调解效应的有效估计。 关于（1），我们最近观察到，在常规回归方法中为二元结果做出的“罕见结果假设”可能会导致实际信息在实践中。我们将提出一个自然直接和间接效应的精确估计量，其理论有效性不依赖于结果的稀有性（或共同点）。这将是针对二进制和连续调解人的，在后一种情况下，将用于嵌套的反事实结果可能性。 对于主题（2），我们计划通过在具有吸引人的两个回归模型上构建贝叶斯调解方法。首先，我们提出一个潜在的多元学生-T模型，作为二进制结果和可能的多个二元介体的精确中介方法的基础。其次，我们旨在根据贝叶斯成瘾性回归树（BART）模型制定灵活的调解策略。中介公式将用于表达从两个模型规范中的自然中介效应。 当前，当实质性知识仅足以完成此任务时，目前缺乏有关如何选择协变量进行调整的协变量的信息。在主题（3）中，我们将针对针对非介导环境引入的两种众所周知的混杂选择算法，并研究如何将它们用于调解环境，以产生直接和间接效应的无偏和有效估计器。 这三个主题都将通过模拟研究进行理论发展和验证。将创建开放式软件，并将提供全面的示例，以增加实质性研究人员的吸收。包括，该计划是关于因果调解分析的几个重要方面的原始和创新统计发展。预计它将在许多应用研究领域（例如社会科学，流行病学）中提高从这种类型的分析获得的结果的可靠性。