Faculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Stat Med. 2012 Feb 20;31(4):383-96. doi: 10.1002/sim.4453.
Recent years have witnessed new innovation in Bayesian techniques to adjust for unmeasured confounding. A challenge with existing methods is that the user is often required to elicit prior distributions for high-dimensional parameters that model competing bias scenarios. This can render the methods unwieldy. In this paper, we propose a novel methodology to adjust for unmeasured confounding that derives default priors for bias parameters for observational studies with binary covariates. The confounding effects of measured and unmeasured variables are treated as exchangeable within a Bayesian framework. We model the joint distribution of covariates by using a log-linear model with pairwise interaction terms. Hierarchical priors constrain the magnitude and direction of bias parameters. An appealing property of the method is that the conditional distribution of the unmeasured confounder follows a logistic model, giving a simple equivalence with previously proposed methods. We apply the method in a data example from pharmacoepidemiology and explore the impact of different priors for bias parameters on the analysis results.
近年来，贝叶斯技术在调整未测量混杂方面出现了新的创新。现有方法的一个挑战是，用户通常需要为高维参数引出先验分布，这些参数用于建模竞争偏差情况。这可能会使方法变得复杂。在本文中，我们提出了一种新的方法来调整未测量的混杂，为具有二分类协变量的观察性研究中的偏差参数推导出默认先验。在贝叶斯框架内，将测量和未测量变量的混杂效应视为可交换的。我们通过使用具有成对交互项的对数线性模型来对协变量的联合分布进行建模。分层先验限制了偏差参数的幅度和方向。该方法的一个吸引人的特性是，未测量混杂的条件分布遵循逻辑模型，与先前提出的方法具有简单的等价性。我们将该方法应用于药物流行病学中的一个数据示例，并探讨了偏差参数的不同先验对分析结果的影响。
