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Assessment of Treatment Effects in High-Dimensional, Routine Care Claims Data

高维常规护理索赔数据中的治疗效果评估

基本信息

批准号：
8037863
负责人：
Sebastian G. Schneeweiss
金额：
$ 133.18万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
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：
2010-09-22 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8037863
关键词：
Address Adult Algorithms American Anticoagulants Anticoagulation Calibration Cardiovascular Agents Cardiovascular Diseases Cardiovascular system Caring Characteristics Clinical Code Cohort Studies Comorbidity Complex Computer software Data Data Set Databases Deposition Diagnostic Effectiveness Electronics Future General Population Head Health Planning Healthcare Healthcare Systems Hospital Costs Hospitals Inpatients Insurance Internet Journals Libraries Medicare/Medicaid Medicine Methodology Methods Modeling Morbidity - disease rate Outcome Patients Performance Pharmaceutical Preparations Population Population Study Primary Health Care Procedures Process Publications Publishing Randomized Research Research Personnel Safety Scoring Method Selection Bias Severity of illness Simvastatin Specific qualifier value Surveys Testing clinically relevant comparative effectiveness cost data mining effectiveness research ezetimibe hypercholesterolemia improved innovation lectures mortality novel novel strategies outcome forecast percutaneous coronary intervention performance tests preference programs public health relevance routine care simulation symposium tool treatment effect web page

项目摘要

DESCRIPTION (provided by applicant): Data on the comparative effectiveness of medications are limited because few head-to-head trials are available and most of them do not represent the general population or real-world practice. Comparative effectiveness research using non-randomized healthcare data can provide critical evidence on the effectiveness and safety of medications and procedures in routine care. While such studies in large electronic healthcare databases can provide expedited and less costly evidence on drug effects in routine care, the conventional confounding adjustment methods that rely on a small number of investigator-specified confounders often fail to produce unbiased results. In recent years three novel methodologies have shown promise to overcome these limitations. Their performance, however, has never been compared with each other in real world comparative effectiveness studies: 1) Combine claims data studies with detailed clinical surveys in subpopulations and use this information for improved confounding control through Propensity Score Calibration (PSC). 2) Empirically identify and prioritize very large numbers of potential confounders and adjusting for them using propensity score methods, called high-dimensional propensity score adjustment (hd-PS). 3) Identify a quasi-random process in the healthcare system that influences treatment choice beyond patient characteristics, e.g. prescriber preference, and apply instrumental variable analysis (IVA). Although long know to economists, IVA is fairly new to comparative effectiveness research. One in three American adults has a cardiovascular condition and the total inpatient cost for such conditions approximates one fourth of the total cost of hospital care in the US. The lack of good comparative effectiveness information is a significant limitation for improving care. The performance of the 3 novel approaches will be tested in three cardiovascular example studies, including (a) Vytorin vs. statin use alone, (b) high vs. low intensity statin therapy after MI, and (c) short and medium-term effectiveness of anticoagulation therapies during percutaneous coronary interventions. Specifically, we will: Aim 1: Implement three novel approaches to improved confounding control in comparative effectiveness research using relevant cardiovascular example studies, Aim 2: Compare performance of the three approaches and improve their implementation, Aim 3: Disseminate methods and provide internet support for free analysis software and result libraries. This project will meaningfully improve methodologies for comparative effectiveness research in cardiovascular medicine using a wide array of healthcare databases. After completion of this project a library of validated algorithms will be available on an interactive web-portal that supports applications and is a depository of supplemental results. PUBLIC HEALTH RELEVANCE: The use of longitudinal healthcare databases is a potentially powerful tool to evaluate the comparative effectiveness of cardiovascular medications as used in routine care. However, conventional confounder adjustment methods that rely on a limited number of investigator-specific covariates often fail to produce unbiased results. We will implement and rigorously evaluate three novel analytic methods to enhance causal interpretation of the effectiveness and safety of commonly used medications.

描述（由申请人提供）：关于药物比较有效性的数据有限，因为很少有头对头试验可用，而且大多数试验并不代表一般人群或现实世界的实践。使用非随机医疗数据的比较有效性研究可以为常规护理中药物和程序的有效性和安全性提供关键证据。虽然大型电子医疗保健数据库中的此类研究可以提供关于常规护理中药物作用的快速且成本较低的证据，但依赖于少数研究者指定的混杂因素的传统混杂调整方法通常无法产生公正的结果。近年来，三种新颖的方法有望克服这些限制。然而，它们的表现从未在现实世界的比较有效性研究中进行过相互比较：1) 将索赔数据研究与亚人群的详细临床调查相结合，并使用此信息通过倾向评分校准 (PSC) 改进混杂控制。 2）根据经验识别大量潜在混杂因素并对其进行优先级排序，并使用倾向评分方法对其进行调整，称为高维倾向评分调整（hd-PS）。 3) 确定医疗保健系统中影响患者特征之外的治疗选择的准随机过程，例如处方者偏好，并应用工具变量分析（IVA）。尽管经济学家早已知道，IVA 对于比较有效性研究来说还是相当陌生的。三分之一的美国成年人患有心血管疾病，此类疾病的住院总费用约占美国医院护理总费用的四分之一。缺乏良好的比较效果信息是改善护理的一个重大限制。这 3 种新方法的性能将在三项心血管实例研究中进行测试，包括 (a) Vytorin 与单独使用他汀类药物的比较，(b) MI 后高强度他汀类药物治疗与低强度他汀类药物治疗的比较，以及 (c) 经皮冠状动脉介入治疗期间抗凝治疗的短期和中期有效性。具体来说，我们将：目标 1：使用相关心血管实例研究在比较有效性研究中实施三种新方法来改进混杂控制，目标 2：比较三种方法的性能并改进其实施，目标 3：传播方法并为免费分析软件和结果库提供互联网支持。该项目将利用广泛的医疗保健数据库，有意义地改进心血管医学比较有效性研究的方法。该项目完成后，将在交互式门户网站上提供经过验证的算法库，该门户支持应用程序并且是补充结果的存储库。公共卫生相关性：使用纵向医疗保健数据库是评估常规护理中使用的心血管药物的相对有效性的潜在强大工具。然而，依赖于有限数量的研究者特定协变量的传统混杂因素调整方法通常无法产生公正的结果。我们将实施并严格评估三种新颖的分析方法，以增强对常用药物有效性和安全性的因果解释。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bias in comparative effectiveness studies due to regional variation in medical practice intensity: a legitimate concern, or much ado about nothing?

DOI：
10.1161/circoutcomes.112.966093
发表时间：
2012-09-01
期刊：
Circulation. Cardiovascular quality and outcomes
影响因子：
0
作者：
Huybrechts KF;Seeger JD;Rothman KJ;Glynn RJ;Avorn J;Schneeweiss S
通讯作者：
Schneeweiss S

Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases.

DOI：
10.1016/j.csda.2013.10.018
发表时间：
2014-04
期刊：
COMPUTATIONAL STATISTICS & DATA ANALYSIS
影响因子：
1.8
作者：
Franklin, Jessica M.;Schneeweiss, Sebastian;Polinski, Jennifer M.;Rassen, Jeremy A.
通讯作者：
Rassen, Jeremy A.

Incorporating linked healthcare claims to improve confounding control in a study of in-hospital medication use.

DOI：
10.1007/s40264-015-0292-x
发表时间：
2015-06
期刊：
DRUG SAFETY
影响因子：
4.2
作者：
Franklin, Jessica M.;Eddings, Wesley;Schneeweiss, Sebastian;Rassen, Jeremy A.
通讯作者：
Rassen, Jeremy A.