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

基本信息

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
项目类别：
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

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

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项目摘要

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）主要涉及估算替代治疗对结局的因果影响。为此，观察数据库是有关患者水平治疗和结果的有前途的信息来源。但是，观察数据分析容易出现选择偏见或通过适应症混淆，这是由于接受不同治疗的患者观察到的和未观察到的危险因素的差异，这会使治疗的因果效应变得复杂。仪器变量（IV）方法是可以解决这些挑战并有助于估算此类数据的因果效应的最强大方法之一，但是这些方法对CER的利用不足。该应用的目的是通过克服采用这些强大的CER方法的三个重要障碍来增加对仪器变量方法的适当利用。适当使用IV方法用于选择良好仪器和适当估计的方法。一个好的仪器必须1）引起治疗选择的实质变化（即“强”），但2）除了通过治疗选择（即“排除限制”）之外，不影响结果。尽管已经研究了使用弱工具的后果，但违反排除限制的后果尚未得到充分理解。即使在传统的同质治疗效果的假设下，也开发了几种新的IV方法。知道哪种方法适合任何特定应用程序仍然具有挑战性。默认值是使用两个阶段的最小二乘，但是CER常见的许多情况都需要替代方法，例如近乎匹配的匹配或两个阶段的残留包含。该应用程序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。如果治疗效果是异质的，则静脉分析的结果可能会推广到错误的亚群，因为这些效果依赖于分析师的IV选择，并且很难出于临床和政策目的来解释。我们还将开发出新的IV方法，以解决治疗效应异质性并为CER产生可解释的结果。由于无法使用易于实现的软件或统计代码，因此CER的许多当前应用并未充分利用近期的IV方法学进步，这导致了IV科学分析对实践的延迟。因此，我们将制定相关的统计代码，以帮助从业者使用常见的统计软件包实施这些方法，并通过前列腺癌和心血管疾病的经验例子说明这些方法。公共卫生相关性：比较有效性研究（CER）主要涉及估算替代治疗对结果的因果影响。为此，观察数据库是有关患者水平治疗和结果的有前途的信息来源。但是，观察数据分析容易出现选择偏见或通过适应症混淆，这是由于接受不同治疗的患者观察到的和未观察到的危险因素的差异，这会使治疗的因果效应变得复杂。仪器变量（IV）方法是可以解决这些挑战并有助于估算此类数据的因果效应的最强大方法之一，但是这些方法对CER的利用不足。该应用的目的是通过克服采用这些强大方法的CER的三个重要障碍来增加对IV方法的适当利用。