Analyzing Complex Healthcare Data to Determine Causality of Observed Drug Effects

分析复杂的医疗数据以确定观察到的药物作用的因果关系

• 批准号: 7767483
• 负责人: Sebastian G. Schneeweiss
• 金额: $ 45.75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7767483
• 关键词: Address; Algorithms; Clinical; Code; Complex; Computerized Medical Record; Data; Data Sources; Databases; Distributed Databases; Effectiveness; Elderly; Epidemiologic Methods; Epidemiology; Etiology; Evaluation; Foundations; Head; Health Planning; Healthcare; Heterogeneity; Individual; Insurance; Internet; Knowledge; Lead; Libraries; Marketing; Medical Informatics; Medicare/Medicaid; Medicine; Methods; Mining; Modeling; Natural Language Processing; Outcome; Patients; Performance; Pharmaceutical Preparations; Phase; Physicians; Placebos; Population; Pregnant Women; Process; Publications; Randomized; Research; Research Personnel; Safety; Scoring Method; Sentinel; Severity of illness; Solid; Solutions; Specific qualifier value; Speed; Staging; Structure; Subgroup; Techniques; Testing; Therapeutic; Time; To specify; Training; base; biomedical informatics; comparative; comparative effectiveness; compare effectiveness; data mining; improved; indexing; lectures; novel; outcome forecast; patient privacy; programs; routine care; symposium; treatment effect

DESCRIPTION (provided by applicant): Epidemiologic analyses of health care data can provide critical evidence on the effectiveness and safety of therapeutics. This is particularly vital during the transition from the point of regulatory approval through the early marketing of new drugs, a time when physicians, regulators and payers are all struggling with incomplete data. Health plans pay for these drugs without knowing how their effectiveness and safety compares with established alternatives, as new compounds are tested against placebos rather than active agents, and tested only in selected patients. Non-randomized studies in large healthcare databases can provide fast and less costly evidence on drug effects. However, conventional adjustment methods that rely on a small number of investigator-specified confounders often fail and may produce biased results. We propose and have preliminary evidence that employing modern medical informatics algorithms that structure and search databases to empirically identify thousands of new covariates. These will then enter established propensity score-based models and so make far more effective use of the information contained in health care databases and electronic medical records (EMRs), resulting in more valid causal interpretations of treatment effects. We will: - Develop algorithms that make greater use of information contained in longitudinal claims and EMR databases by empirically identifying thousands of potential confounders. The performance of these approaches will be evaluated in 6 example studies encompassing recent drug safety and comparative effectiveness problems, and will be implemented in multiple large claims databases supplemented by such data as lab values and EMR information in subgroups. -- Develop novel methods for confounding adjustment based on textual information found in EMRs. -- Expand the newly developed mining algorithms into a framework that integrates distributed database networks with uneven information content, similar to the Sentinel Network recently initiated by FDA. This project is likely to produce groundbreaking results at the interface of medicine, biomedical informatics, and epidemiologic methods. After completion of this project a library of documented and validated algorithms will be available to significantly improve confounder control in a range of healthcare databases. The theoretical foundation and the ready-to-use algorithms will likely lead to a fundamental shift in how databases contribute to the fast and accurate assessment of newly-marketed medications.

描述(由申请人提供)： 对卫生保健数据的流行病学分析可以为治疗的有效性和安全性提供关键证据。在从监管审批到新药早期营销的过渡过程中，这一点尤其重要，因为在这个时期，医生、监管机构和支付者都在为不完整的数据而苦苦挣扎。医疗计划为这些药物买单，而不知道它们的有效性和安全性与现有替代品相比如何，因为新化合物是针对安慰剂而不是活性物质进行测试的，并且只在选定的患者身上进行测试。大型医疗保健数据库中的非随机研究可以快速、廉价地提供药物效果的证据。然而，依赖于少量调查者指定的混杂因素的传统调整方法往往失败，并可能产生有偏见的结果。 我们提出并有初步证据表明，利用构建和搜索数据库的现代医学信息学算法来经验性地识别数千个新的协变量。然后，这些将进入已建立的基于倾向评分的模型，从而更有效地利用卫生保健数据库和电子医疗记录(EMR)中包含的信息，从而对治疗效果进行更有效的因果解释。我们会： -开发算法，通过经验确定数千个潜在的混杂因素，更多地利用纵向索赔和电子病历数据库中包含的信息。这些方法的性能将在涵盖最近药物安全和比较有效性问题的6个示例研究中进行评估，并将在多个大型索赔数据库中实施，并在分组中补充实验室值和电子病历信息等数据。 --根据EMR中发现的文本信息，开发混淆调整的新方法。 --将新开发的挖掘算法扩展为一个框架，该框架集成了信息内容参差不齐的分布式数据库网络，类似于FDA最近发起的哨兵网络。 该项目可能在医学、生物医学信息学和流行病学方法方面产生突破性成果。在这个项目完成后，一个记录和验证的算法库将可用来显着改善一系列医疗保健数据库中的混杂控制。理论基础和现成的算法可能会导致数据库如何有助于对新药进行快速和准确的评估的根本转变。