Analyzing Complex Healthcare Data to Determine Causality of Observed Drug Effects

描述（由申请人提供）： 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。在从监管批准到新药早期上市的过渡期间，这一点尤为重要，此时医生，监管机构和付款人都在努力处理不完整的数据。健康计划支付这些药物的费用，但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剂进行测试的，并且只在选定的患者中进行测试。大型医疗数据库中的非随机研究可以提供快速且成本较低的药物效应证据。然而，传统的调整方法，依赖于少量的干扰指定的混杂因素往往失败，并可能产生偏倚的结果。 我们提出并有初步的证据表明，采用现代医学信息学算法，结构和搜索数据库，以经验确定数千个新的协变量。然后，这些将进入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，从而对治疗效果进行更有效的因果解释。我们将： - 开发算法，通过经验识别数千个潜在的混杂因素，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6项包含近期药物安全性和比较有效性问题的示例研究中进行评价，并将在多个大型索赔数据库中实施，这些数据库由亚组的实验室值和EMR信息等数据补充。 --根据EMR中的文本信息开发新的混杂调整方法。 --将新开发的挖掘算法扩展为一个框架，将信息内容不均匀的分布式数据库网络集成在一起，类似于FDA最近发起的Sentinel Network。 这个项目很可能在医学、生物医学信息学和流行病学方法的界面上产生开创性的结果。完成本项目后，将提供一个记录和验证算法的库，以显著改善一系列医疗保健数据库中的混淆控制。理论基础和现成的算法可能会导致数据库如何有助于快速准确地评估新上市药物的根本转变。

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.