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

基本信息

批准号：
8318797
负责人：
Sebastian G. Schneeweiss
金额：
$ 38.34万
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：
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/8318797
关键词：
Address Algorithms Clinical Code Complex Computerized Medical Record Data Data Sources Databases Distributed Databases Effectiveness Elderly woman 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 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

项目摘要

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）：医疗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。这在新药从监管批准到早期营销的过渡期间尤其重要，此时医生、监管机构和付款人都在与不完整的数据作斗争。健康计划在不知道这些药物与现有替代药物的有效性和安全性比较的情况下支付这些药物的费用，因为新化合物是针对安慰剂而不是活性药物进行测试的，并且仅在选定的患者中进行测试。大型医疗保健数据库中的非随机研究可以提供快速且成本较低的药物作用证据。然而，依赖于少数研究者指定的混杂因素的传统调整方法常常会失败，并可能产生有偏差的结果。我们提出并有初步证据表明，采用现代医学信息学算法来构建和搜索数据库，以凭经验识别数千个新协变量。然后，这些将进入已建立的基于倾向评分的模型，从而更有效地利用医疗保健数据库和电子病历（EMR）中包含的信息，从而对治疗效果做出更有效的因果解释。我们将： - 通过凭经验识别数千个潜在的混杂因素，开发算法，更好地利用纵向索赔和 EMR 数据库中包含的信息。这些方法的性能将在涵盖近期药物安全性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，并辅以实验室值和子组中的 EMR 信息等数据。 -- 根据电子病历中的文本信息开发混杂调整的新方法。 ——将新开发的挖掘算法扩展为一个集成信息内容不均匀的分布式数据库网络的框架，类似于FDA最近发起的Sentinel网络。该项目可能会在医学、生物医学信息学和流行病学方法的交叉领域产生突破性的成果。该项目完成后，将提供一个经过记录和验证的算法库，以显着改善一系列医疗保健数据库中的混杂因素控制。理论基础和即用型算法可能会导致数据库如何有助于快速准确地评估新上市药物的方式发生根本性转变。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

调整工具变量对效应估计的偏差和精度的影响。

DOI：
10.1093/aje/kwr364
发表时间：
2011-12-01
期刊：
American journal of epidemiology
影响因子：
5
作者：
J. Myers;J. Rassen;J. Gagne;K. Huybrechts;S. Schneeweiss;K. Rothman;M. Joffe;R. Glynn
通讯作者：
R. Glynn

Drugs that inhibit gastric acid secretion may alter the course of inflammatory bowel disease.

抑制胃酸分泌的药物可能会改变炎症性肠病的病程。