Advancing Instrumental Variable Methods in Comparative Effectiveness Research

描述（由申请人提供）：比较有效性研究（CER）主要涉及估计替代治疗对结局的因果影响。为此，观察性数据库是关于患者水平治疗和结局的有希望的信息来源。然而，观察性数据分析容易出现选择偏倚或适应症混淆，这是由于接受不同治疗的患者之间观察到的和未观察到的风险因素水平存在差异，并且使治疗因果效应的推断复杂化。工具变量（IV）方法是最强大的方法之一，可以解决这些挑战，并帮助估计因果关系的影响，从这些数据，但这些方法是未充分利用的CER。该应用程序的目标是通过克服三个重要的障碍，通过这些强大的方法CER增加工具变量方法的适当利用。 CER中IV方法的正确使用取决于选择良好的工具和适当的估计。一个好的工具必须：1）在治疗选择中引起实质性变化（即“强有力”），但2）除了通过治疗选择（即“排除限制”）外，不影响结果。虽然已经调查了使用弱工具的后果，但对违反排除限制的后果还没有很好的理解。即使在传统的治疗效果均匀的假设下，也正在开发几种新的IV方法。知道哪种方法适合任何特定的应用仍然是一个挑战。默认情况下使用两阶段最小二乘法，但CER常见的许多情况需要替代方法，如远近匹配或两阶段残差包含。该应用程序旨在通过应用工具变量分析来应对这些挑战，旨在为CER的应用从业人员提供适当的指导。如果治疗效应具有异质性，则IV分析的结果可能推广至错误的亚群，因为这些效应取决于分析员对IV的选择，并且难以出于临床和政策目的进行解释。我们还将开发新的IV方法，解决治疗效果异质性，并为CER生成可解释的结果。目前CER的许多应用程序没有充分利用最近的IV方法的进步，由于没有现成的可执行的软件或统计代码，导致IV分析的科学转化为实践的延迟。因此，我们将开发相关的统计代码，以帮助从业人员使用常见的统计软件包来实现这些方法，并通过前列腺癌和心血管疾病的实证例子来说明这些方法。 公共卫生相关性：比较有效性研究（CER）主要涉及估计替代治疗对结果的因果影响。为此，观察性数据库是关于患者水平治疗和结局的有希望的信息来源。然而，观察性数据分析容易出现选择偏倚或适应症混淆，这是由于接受不同治疗的患者之间观察到的和未观察到的风险因素水平存在差异，并且使治疗因果效应的推断复杂化。工具变量（IV）方法是最强大的方法之一，可以解决这些挑战，并帮助估计因果关系的影响，从这些数据，但这些方法是未充分利用的CER。本申请的目标是通过克服采用这些强大方法进行CER的三个重要障碍来提高IV方法的适当利用率。

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of instrumental variables methods by overcoming three important barriers to adoption of these powerful methods for CER. Appropriate use of IV methods for CER hinges on selecting good instruments and appropriate estimation. A good instrument must 1) induce substantial variation in treatment choices (i.e. be "strong") but 2) not affect outcomes except through treatment choices (i.e. the "exclusion restriction"). While the consequences of using weak instruments have been investigated, the consequences of violating the exclusion restriction are not well understood. Even under the traditional assumption of a homogenous treatment effect, several new IV approaches are being developed. Knowing which method is appropriate for any particular application remains challenging. The default has been to use two-stage least squares, but many situations common to CER require alternative approaches such as near-far matching or two-stage residual inclusion. This application aims to address these challenges with applying instrumental variables analysis with a goal of providing applied practitioners of CER with appropriate guidance. Results of IV analyses may be generalized to the wrong subpopulations if treatment effects are heterogeneous as these effects become dependent on the analyst's choice of IV(s) and are difficult to interpret for clinical and policy purposes. We will also develop novel IV approaches that address treatment effect heterogeneity and generate interpretable results for CER. Many current applications of CER do not take full advantage of recent IV methodological advances, due to unavailability of readily implementable software or statistical code, resulting in delays in the translation of the science of IV analysis to practice. Therefore, we will develop relevant statistical code to help practitioners implement these methods using common statistical software packages and illustrate the methods through empirical examples in prostate cancer and cardiovascular disease. PUBLIC HEALTH RELEVANCE: Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of IV methods by overcoming three important barriers to adoption of these powerful methods for CER.