Accounting for confounding bias and heterogeneity in comparative effectiveness

考虑比较有效性中的混杂偏差和异质性

• 批准号: 8037453
• 负责人: Alistair James O'MALLEY
• 金额: $ 149.22万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-27 至 2013-09-26
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8037453
• 关键词: Abdominal Aortic Aneurysm; Accounting; Address; Area; Bipolar Depression; Bipolar Disorder; Caring; Clinical; Clinical Services; Combined Modality Therapy; Comorbidity; Data; Data Sources; Databases; Diagnostic; Drug Formulations; Effectiveness; Enrollment; Equation; Experimental Designs; Goals; Health Care Costs; Health Technology; Healthcare; Heterogeneity; Individual; Life; Medicaid; Medical Device; Medicare; Medicine; Mental Health; Meta-Analysis; Methods; Modeling; Morbidity - disease rate; Observational Study; Operative Surgical Procedures; Patient Representative; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Physicians; Placebos; Population; Provider; Randomized; Randomized Controlled Trials; Registries; Relative (related person); Reliance; Research; Research Personnel; Safety; Sampling; Societies; Source; System; Techniques; Testing; Therapeutic; Time; Treatment Effectiveness; Treatment Efficacy; abstracting; administrative database; alternative treatment; arm; cancer surgery; clinically significant; comparative effectiveness; compare effectiveness; cost; design; effectiveness research; improved; interest; novel; novel strategies; payment; programs; randomized trial; repaired; technological innovation; treatment as usual

DESCRIPTION (provided by applicant): There is increased interest in comparative effectiveness research as a way of informing clinicians, payers, and policymakers about the relative effectiveness of different treatments with the goal of maximizing benefit to patients and value to payers. Although, randomized controlled trials (RCTs) are currently relied upon to provide information on efficacy of new treatments they are often a poor source of data on comparative effectiveness. RCTs typically enroll carefully selected populations that are not representative of all individuals using the new treatments and often compare a new treatment only with placebo rather than with other commonly-used treatments for the same conditions. Other data sources, including clinical registries developed by professional societies and large administrative databases, provide an opportunity to investigate treatment efficacy and safety in a variety of clinical settings beyond those used for RCTs. However, with these observational data comes the problem of confounding bias from uncontrolled selection into treatment. Thus, improved methods are needed to address the limitations of currently available comparative effectiveness data, including non-representative populations enrolled in RCTs, lack of comparisons between commonly-used treatments in RCTs, and confounding bias in observational studies. Such problems are common across a variety of areas of medicine. This proposal seeks to develop novel and generalizable methods for addressing these problems. Specifically, we will develop novel approaches for combining data from randomized trials, registries and/or claims-based data (taking advantage of the strengths of both RCT and observational data); extend the latest techniques for instrumental variable analysis; and develop novel simultaneous equation models to account for confounding that are less sensitive to assumptions than currently-used methods. In so doing, we will apply these methods to three important clinical examples: treatments for bipolar disorder for patients with psychiatric comorbidity, reformulations of existing psychiatric drug treatments, and the surgical repair for abdominal aortic aneurysm (AAA). PUBLIC HEALTH RELEVANCE: New methods are developed to address the limitations of currently available comparative effectiveness analysis, including non-representative populations enrolled in randomized controlled trials, lack of comparisons between commonly-used treatments in RCTs, and confounding bias in observational studies. Such problems are common across a variety of areas of medicine including mental health, surgery, cancer, and medical devices. The proposed methods will help to better inform clinicians, payers, and policymakers about the relative effectiveness of different treatments with the goal of maximizing benefit to patients and value to payers.

描述（由申请人提供）：人们对比较有效性研究的兴趣越来越大，这是一种向临床医生、付款人和政策制定者告知不同治疗的相对有效性的方式，目的是最大限度地提高患者的获益和付款人的价值。尽管目前依赖随机对照试验（RCT）来提供有关新治疗有效性的信息，但它们通常是比较有效性的不良数据来源。随机对照试验通常招募精心挑选的人群，这些人群并不能代表使用新治疗的所有个体，并且通常只将新治疗与安慰剂进行比较，而不是与其他常用的治疗方法进行比较。其他数据来源，包括专业协会开发的临床登记和大型管理数据库，提供了在RCT之外的各种临床环境中研究治疗疗效和安全性的机会。然而，随着这些观察数据的出现，来自未控制的选择到治疗的混杂偏倚的问题。因此，需要改进方法来解决目前可用的比较有效性数据的局限性，包括RCT中招募的非代表性人群，RCT中常用治疗之间缺乏比较，以及观察性研究中的混杂偏倚。这些问题在医学的各个领域都很常见。该提案旨在开发新的和可推广的方法来解决这些问题。具体来说，我们将开发新的方法来结合来自随机试验，登记和/或基于索赔的数据（利用RCT和观察数据的优势）的数据;扩展工具变量分析的最新技术;并开发新的联立方程模型，以解释对假设不太敏感的混杂因素。在这样做时，我们将这些方法应用于三个重要的临床实例：治疗双相情感障碍患者的精神科合并症，现有的精神科药物治疗的重新制定，以及腹主动脉瘤（AAA）的手术修复。 公共卫生相关性：新方法的开发是为了解决目前可用的比较有效性分析的局限性，包括随机对照试验中招募的非代表性人群，RCT中常用治疗之间缺乏比较，以及观察性研究中的混杂偏倚。这些问题在医学的各个领域都很常见，包括精神健康，外科手术，癌症和医疗器械。所提出的方法将有助于更好地告知临床医生，付款人和政策制定者不同治疗的相对有效性，以最大限度地提高患者的利益和付款人的价值。

项目成果

Reporting of Baseline Characteristics to Accompany Analysis by Instrumental Variables.

报告基线特征以伴随工具变量分析。

• DOI: 10.1097/ede.0000000000000914
• 发表时间: 2018
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: MacKenzie,ToddA;O'Malley,AJames;Bekelis,Kimon
• 通讯作者: Bekelis,Kimon

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.

使用工具变量来估计COX的比例危害回归，但会受到添加混杂的影响。

• DOI: 10.1007/s10742-014-0117-x
• 发表时间: 2014-06
• 期刊: HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY
• 影响因子: 1.5
• 作者: MacKenzie, Todd A;Tosteson, Tor D;Morden, Nancy E;Stukel, Therese A;O'Malley, A James
• 通讯作者: O'Malley, A James

Patient Centered Hazard Ratio Estimation Using Principal Stratification Weights: Application to the NORCCAP Randomized Trial of Colorectal Cancer Screening

使用主分层权重以患者为中心的风险比估计：在 NORCCAP 结直肠癌筛查随机试验中的应用

• DOI: 10.1353/obs.2016.0004
• 发表时间: 2016
• 期刊: Observational Studies
• 作者: T. Mackenzie;M. Løberg;A. J. O’Malley
• 通讯作者: A. J. O’Malley