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因果调解统计方法的发展。中介分析是一种越来越流行的技术，用于估计暴露(治疗)的影响，前提是暴露与结果之间的因果路径上存在中介变量。这样的分析旨在将总暴露效应分解为通过调解人产生的间接效应和避开中介路径的直接效应。 该计划分为三个主题，以解决因果调解中的相关建模问题：(1)为二元结果开发基于回归的精确调解方法；(2)为因果调解的目的调整现有的两个贝叶斯回归模型；(3)调查混杂因素选择以有效估计调解效果。 关于(1)，我们最近观察到，在传统回归方法中对二元结果所作的“罕见结果假设”可能会在实践中导致错误的推断。我们将为自然的直接和间接影响提出一个精确的估计器，其理论有效性不依赖于结果的稀缺性(或共性)。这将对二元调解人和连续调解人都实现，在后一种情况下，将对嵌套的反事实结果概率使用Logit正态表示。 对于主题(2)，我们计划通过构建两个具有吸引人的功能的回归模型来扩展贝叶斯调解方法。首先，我们提出了一个潜在的多变量学生-t模型，作为对一个二元结果和可能的多个二元中介的精确中介方法的基础。其次，提出了一种基于贝叶斯加性回归树(BART)模型的灵活中介策略。调解公式将用于表达两种模型规范的自然调解效果。 当仅有实质性知识不足以完成这项任务时，目前缺乏关于如何在因果调解模型中选择要调整的协变量的信息。在主题(3)中，我们将针对两个为非中介语境引入的众所周知的混杂选择算法，并研究如何针对中介语境对它们进行修改，以产生对直接和间接影响的无偏见和有效的估计值。 所有这三个主题都需要理论发展和通过模拟研究进行验证。将创建开放获取软件，并提供全面的例子，以增加实质性研究人员的理解。总而言之，本节目是关于因果调解分析的几个重要方面的原创和创新的统计发展。预计这将增加这类分析在许多应用研究领域(如社会科学、流行病学)中获得的结果的可靠性。