Institute for Evaluation of Labour Market and Education Policy, Uppsala, Sweden.
Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium.
Stat Med. 2019 Oct 15;38(23):4749-4760. doi: 10.1002/sim.8332. Epub 2019 Aug 2.
Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control studies and co-twin-control studies, it is common to use conditional logistic regression. In this model, all cluster-constant covariates are absorbed into a cluster-specific intercept, whereas cluster-varying covariates are adjusted for by explicitly adding these as explanatory variables to the model. In this paper, we propose a doubly robust estimator of the exposure-outcome odds ratio in conditional logistic regression models. This estimator protects against bias in the odds ratio estimator due to misspecification of the part of the model that contains the cluster-varying covariates. The doubly robust estimator uses two conditional logistic regression models for the odds ratio, one prospective and one retrospective, and is consistent for the exposure-outcome odds ratio if at least one of these models is correctly specified, not necessarily both. We demonstrate the properties of the proposed method by simulations and by re-analyzing a publicly available dataset from a matched case-control study on induced abortion and infertility.
流行病学研究通常旨在估计二元暴露与二元结局之间的关联，同时调整一组协变量（例如，混杂因素）。当数据呈聚类时，例如匹配病例对照研究和同卵双胞胎对照研究，通常使用条件逻辑回归。在该模型中，所有聚类常数协变量都被吸收到聚类特定的截距中，而聚类变化的协变量则通过明确将其作为解释变量添加到模型中进行调整。本文提出了条件逻辑回归模型中暴露-结局比值的双重稳健估计量。该估计量可以防止由于模型中包含聚类变化的协变量的部分指定不正确而导致比值估计量出现偏差。双重稳健估计量使用两个用于比值的条件逻辑回归模型，一个前瞻性的，一个回顾性的，如果至少有一个模型正确指定，则该估计量对暴露-结局比值是一致的，不一定两个模型都要正确指定。我们通过模拟和重新分析诱导性流产和不育症的匹配病例对照研究的公开数据集来证明所提出方法的性质。
