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

 描述（由申请人提供）：健康结果和政策研究以及其他学科的一个共同目标是评估干预措施的可能因果影响，这被广泛定义为政策变化，计划参与或医疗。在健康结果研究中，许多数据集都是观察性的，因为随机化通常不可行或不符合伦理。此外，这些数据集中有许多是从大概率调查中获得的。由于以下事实，这给推断因果关系带来了重大挑战：（1）干预组结果的差异可能是由于干预前协变量的差异造成的;（2）由于复杂的抽样设计，它们总是涉及不平等的权重。基于倾向评分的调整被广泛用于减少观察性研究中协变量的混杂偏倚。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关键的方法学差距，并且研究人员在使用调查数据时解释因果效应的最佳实践方面存在着大量的困惑。该项目的总体目标是开发一种系统的和统计上有效的方法，采用因果推理技术与复杂的医疗调查数据。由于使用抽样权重，所提出的方法在存在异质性治疗效果的情况下特别有用，即不同群体可能对政策变化或某种医疗的引入作出不同的反应。本研究将达致四个具体目的：（1）发展一个以潜在结果为基础的理论架构，以简化复杂调查中的因果推论;（2）发展倾向评分及调查设计调整估计量，包括加权、分层及配对估计量;（三）进行广泛的模拟研究，以评估各种估算方法在不同实际情况下的表现，并开发一个统计软件（4）将所提出的方法应用于比较创伤护理研究的真实的调查。本研究通过将非调查数据的常用倾向评分调整扩展到复杂的抽样设计，有望填补医疗保健政策和治疗效果评价研究的关键空白。本研究的发现将有助于促进AHRQ的使命，以产生更准确的证据，为卫生保健计划的评估和改善目前的做法，比较健康结果的研究。一个重要的贡献是这种通用的方法，它将是广泛适用的，可以使政府机构，政策制定者，社会，政治和健康科学研究人员受益，在这些情况下，调查数据是比较结果研究和方案政策评估的重要来源。

 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.