Advancing Instrumental Variable Methods in Comparative Effectiveness Research

推进比较有效性研究中的工具变量方法

• 批准号: 8036881
• 负责人: ANIRBAN BASU
• 金额: $ 1.95万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-28 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8036881
• 关键词: Address; Adoption; Affect; Arts; Cardiovascular Diseases; Characteristics; Clinical; Code; Computer software; Data; Data Analyses; Databases; Evaluation; Exclusion; Goals; Health; Healthcare; Heterogeneity; Inferior; Least-Squares Analysis; Literature; Malignant Neoplasms; Malignant neoplasm of prostate; Medical; Methods; Modality; Observational Study; Outcome; Patients; Physicians; Policies; Policy Analysis; Procedures; Prostate; Regression Analysis; Research; Research Personnel; Residual state; Risk Factors; Science; Scoring Method; Selection Bias; Source; Staging; Statistical Methods; Techniques; Testing; Translations; Treatment outcome; Variant; Work; abstracting; alternative treatment; base; comparative; comparative effectiveness; computer code; effectiveness research; geographic difference; improved; instrument; novel; research study; simulation; technique development; treatment effect

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.

描述(由申请人提供)：比较有效性研究(CER)主要涉及对替代治疗对结果的因果影响的估计。为此，观察性数据库是关于患者水平的治疗和结果的一个有希望的信息来源。然而，观察性数据分析容易出现选择偏差或适应症混淆，这是由于接受不同治疗的患者观察到的和未观察到的风险因素水平不同而引起的，并使对治疗的因果效应的推断复杂化。工具变量(IV)方法是能够应对这些挑战并帮助从这些数据中估计因果效应的最强大的方法之一，但这些方法在CER中没有得到充分利用。本应用程序的目标是通过克服采用这些强大的CER方法的三个重要障碍，提高工具变量方法的适当利用率。正确使用IV方法进行CER，关键在于选择好的仪器和适当的估计。一个好的工具必须1)在治疗选择上引起很大的变化(即“强有力的”)，但2)除非通过治疗选择(即“排除限制”)，否则不会影响结果。虽然已经对使用薄弱手段的后果进行了调查，但违反排除限制的后果还没有得到很好的理解。即使在同质治疗效应的传统假设下，仍有几种新的静脉输注方法正在开发中。知道哪种方法适合任何特定的应用程序仍然具有挑战性。默认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，如近-远匹配或两阶段残差包含。本申请旨在通过应用工具变量分析来解决这些挑战，目的是为CER的应用从业者提供适当的指导。如果治疗效果是不同的，则IV分析的结果可能被推广到错误的亚群，因为这些影响变得依赖于分析师对IV的选择(S)，并且难以解释用于临床和政策目的。我们还将开发新的IV方法，解决治疗效果的异质性，并为CER产生可解释的结果。由于没有易于实施的软件或统计代码，CER目前的许多应用程序没有充分利用最新的IV方法学进展，导致IV分析科学转化为实践的延迟。因此，我们将开发相关的统计代码，帮助从业者使用常用的统计软件包来实施这些方法，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)主要涉及对替代疗法对结果的因果影响的估计。为此，观察性数据库是关于患者水平的治疗和结果的一个有希望的信息来源。然而，观察性数据分析容易出现选择偏差或适应症混淆，这是由于接受不同治疗的患者观察到的和未观察到的风险因素水平不同而引起的，这使得对治疗的因果效应的推断复杂化。工具变量(IV)方法是能够应对这些挑战并帮助从这些数据中估计因果效应的最强大的方法之一，但这些方法在CER中没有得到充分利用。本申请的目标是通过克服采用这些强大的CER方法的三个重要障碍，提高IV方法的适当利用率。