Causal Inference for Treatment Effect using Observational Healthcare Data with Unequal Sampling Weights

 描述(由申请人提供)：在健康结果和政策研究以及其他学科中，一个共同的目标是评估干预的可能因果影响，广义上定义为政策变化、项目参与或医疗。在健康结果研究中，许多数据集是观察性的，因为随机化通常是不可行的或不符合道德的。此外，许多这些数据集都是从大概率调查中获得的。这给推断因果关系带来了重大挑战，原因如下：(1)干预组结果的差异可能是由于干预前协变量的不同；(2)由于复杂的抽样设计，它们总是涉及不同的权重。在观察性研究中，基于倾向性分数的调整被广泛用于减少协变量的混淆偏差。但是，在将复杂的调查设计适当地纳入倾向得分分析方面，存在着严重的方法论差距，研究人员在使用调查数据时解释因果关系的最佳实践方面存在很大的困惑。这个项目的首要目标是开发一种系统的和统计上有效的方法，用于使用复杂的医疗调查数据的因果推理技术。由于使用了抽样权重，建议的方法在存在不同的治疗效果时特别有用，即不同的群体可能对政策变化或某种医疗措施的引入做出不同的反应。拟议的研究将实现四个具体目标：(1)开发一个基于潜在结果的理论框架，以简化复杂调查中的因果推断；(2)开发倾向评分和调查设计调整的估计器，包括加权的、分层的和匹配的估计器；(3)进行广泛的模拟研究，以评估各种估计器在不同实际情景下的表现，并为从业者开发统计软件包；以及(4)将建议的方法应用于比较创伤护理研究的真实调查。这项研究有望通过将常用的非调查数据倾向分数调整扩展到复杂的抽样设计，来填补医疗保健政策和治疗效果评估研究的关键空白。这项研究的结果将有助于促进AHRQ的使命，为卫生保健计划评估提供更准确的证据，并改善目前比较健康结果研究的实践。在调查数据是比较结果研究和项目政策评估的重要来源的情况下，这种通用方法具有重要的贡献，它将广泛适用，并可使政府机构、政策制定者以及社会、政治和卫生科学研究人员受益。

 DESCRIPTION (provided by applicant): A common goal in health outcome and policy research, as well as in other disciplines, is to evaluate the possible causal effect of an intervention, which is broadly defined as a policy change, program participation, or a medical treatment. In health outcome research, many datasets are observational since randomization is often not feasible or ethical. Also, many of these datasets are obtained from large probability surveys. This presents major challenges in inferring causal relationships due to the following facts: (1) the differences in outcomes for intervention groups could be due to the differences in covariates prior to the intervention; and (2) they always involve unequal weighting due to complex sampling designs. Propensity score-based adjustments are widely used to reduce the confounding bias of covariates in observational studies. But there is a critical methodological gap with regard to appropriately incorporating complex survey design in propensity score analysis, and there is a significant amount of confusion among researchers regarding the best practice for interpreting causal effect when using survey data. The overarching goal of this project is to develop a systematic and statistically valid approach for employing causal inference techniques with complex healthcare survey data. Because of the use of sampling weights, the proposed methods are particularly useful in the presence of heterogeneous treatment effects, i.e. different groups may respond differently to a policy change or the introduction of a certain medical treatment. The proposed study will achieve four specific aims: (1) Develop a potential-outcome-based theoretical framework to streamline causal inference in complex surveys; (2) Develop both propensity score and survey design adjusted estimators, including weighted, stratified and matched estimators; (3) Conduct extensive simulation studies to evaluate the performance of various estimators under different practical scenarios and develop a statistical software package for practitioners; and (4) Apply the proposed methodology to a real survey for comparative trauma care research. This study is expected to fill a critical gap in healthcare policy and treatment effect evaluation research by extending the commonly used propensity score adjustment for non-survey data to complex sampling designs. Findings of this study will help promote AHRQ's mission to produce more accurate evidence for health care program evaluation and to improve the current practice of comparative health outcome research. A significant contribution is this general purpose methodology which will be widely applicable and can benefit government agencies, policy makers, and social, political and health science researchers, in those situations where survey data are vital sources for comparative outcomes research and program policy evaluation.