Bayesian Methods for Comparative Effectiveness Research with Observational Data

 描述(由申请人提供)：比较有效性研究(CER)依赖于对迅速扩大的观察性数据的分析，这是由于卫生保健提供的日益一体化、电子病历系统的传播以及临床登记数据的发展而成为可能的。这些数据既为旨在提高医疗保健价值的研究提供了非同寻常的机会，也为有意义的研究带来了新的挑战。一个关键的障碍与缺乏可靠的统计方法和工具有关，这些方法和工具可以解决观察性研究中估计治疗效果的多个方面 当治疗效果在不同的亚群和协变量之间可能不同时，定义这些亚群的信息是高维的，有时是无法测量的。目的1)发展新的贝叶斯方法用于大规模观测数据的因果推断：1)估计在测量混杂因子选择中模型不确定性的平均因果效应；2)估计子总体中的平均因果效应，在亚组选择中考虑不确定性。这一新提出的方法概括了现有的方法，因为它不依赖于单个模型的规范，而是通过对多个模型进行平均来估计参数。目的2发展新的贝叶斯方法，用于在存在未测量的混杂因素的情况下评估治疗效果，这些混杂因素在大量观察数据中具有中等的治疗效果。新方法使用工具变量1)确定基本因果参数的分布，而不是平均值；2)通过系统地放松选择偏差假设，将因果参数与子群联系起来。目的3将新方法应用于观察性研究，以提供医疗器械、外科手术和药物治疗领域的新的和完全可重复的知识。AIM 4开发了灵活、高效、健壮、文档完整、用户友好的R库和SAS宏，便于传播我们新开发的方法。我们的新方法，它们对大型行政和临床登记数据的应用，以及它们的传播，将使整个研究界能够以最高的方法学严谨性解决现代CER问题。

 DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) relies upon the analysis of a rapidly expanding universe of observational data made possible by the growing integration of health care delivery, the dissemination of electronic medical records systems, and the development of clinical registries data. These data present both extraordinary opportunities for research aimed at improving value in health care as well as new challenges for meaningful investigation. A critical barrier relates to the lack of sound statistical methods and tools that can address the multiple facets of estimating treatment effects in observational studies when treatment effectiveness may vary across subpopulations and covariate information defining these subgroups is high dimensional and sometimes unmeasured. Aim 1 develops new Bayesian methods for causal inference in large observational data to 1) estimate average causal effects accounting for model uncertainty in the selection of measured confounders and 2) estimate average causal effects in sub-populations accounting for uncertainty in the selection of the subgroups. This newly proposed approach generalizes existing methods because it will not rely on the specification of a single model, but instead will estimate parameters by averaging across several models. Aim 2 develops new Bayesian methods for assessing treatment effects in the presence of unmeasured confounders that moderate treatment effects in large observational data. The new approach uses instrumental variables to 1) identify the distributions, rather than means, of essential causal parameters and 2) link causal parameters to subgroups by systematically relaxing selection bias assumptions. Aim 3 applies the new methods to observational studies to provide new and fully reproducible knowledge in the areas of medical devices, surgical procedures, and pharmaceutical treatments. Aim 4 develops flexible, efficient, robust, well documented, user-friendly R libraries and SAS macros, facilitatin dissemination of our newly developed methods. Our new methods, their applications to large administrative and clinical registry data, and their dissemination will allow the entire research community to address modern CER questions with the highest methodological rigor.