Effectiveness studies with securely pooled healthcare data and adjusted analyses

描述（由申请人提供）：《食品和药物管理局修正案》(FDAAAA) 要求 FDA 在 2012 年之前获得覆盖 1 亿人生命的多个分布式大型索赔和电子医疗记录数据库的访问权限。该数据库将提供足够的规模来研究极其罕见的事件，并为利用常规护理数据进行广泛的安全性和有效性比较研究提供平台。这将包括在许多试验中代表性不足的患者，包括患有多种疾病的老龄化人群。数据隐私要求将限制详细患者信息的共享，尽管此类信息对于混杂因素的多变量调整和有效的因果推断至关重要。另一个未解决的主要问题是如何结合来自各种医疗保健数据库的异构信息内容，以最大限度地实现混杂控制。该提案旨在改进汇集异质个体水平电子医疗数据的方法，以便在不共享私人患者数据的情况下进行完全多变量调整的汇总分析。 ---我们将开发和测试方法和算法，用于汇集相似和异构数据元素，结合来自多个医疗保健提供者的索赔信息，然后用电子病历和实验室值增强这些索赔。这些方法对于编码标准是中立的，并且对于异构数据库结构是鲁棒的。 ---我们将使用这些方法进行三项示例研究，由于暴露频率低、患者亚组小、结果罕见或这些因素的组合，每项研究都需要汇总数据：（1）急性冠脉综合征后使用高效他汀类药物与低效他汀类药物对心肌梗死和心血管死亡的有效性； (2) TNF 抑制剂在类风湿性关节炎患者中在减少止痛药使用以及改善实验室值和 X 射线诊断方面的有效性； (3) 在质子泵抑制剂存在的情况下，对于急性冠脉综合征和/或经皮冠状动脉介入治疗的患者，氯吡格雷的有效性降低，与单独使用氯吡格雷相比，可能导致再梗死率和死亡率增加。 ---我们将在统计和操作上探讨个人层面数据的汇集何时会优于聚合层面的荟萃分析，并检验聚合层面的荟萃分析在大多数情况下将产生基本相似的点估计的假设。 --- 我们将发布并提供所有开发方法的 SAS 代码，并向相关研究人员群体提供培训课程，以扩大范围并确保所开展工作的持久影响。这个为期两年的项目将极大地推进从不同医疗保健数据库中汇集个人信息的方法。这项工作将允许进行比较有效性和安全性分析，这对付款人（医疗保险）和监管机构（FDA）来说是高度优先的，并将提供多变量调整后的结果，而不会对患者隐私构成威胁。重点是广泛和快速的实际适用性。

Description (provided by applicant): The Food and Drug Administration Amendments Act (FDAAA) requires FDA to obtain access to multiple distributed large claims and electronic medical records databases covering 100 million lives by 2012. The pooled database will provide sufficient size to study extremely rare events and a platform for extensive comparative safety and effectiveness research with routine care data. This will include patients underrepresented in many trials, including aging populations with multiple morbidities. Data privacy requirements will limit sharing of detailed patient information, although such information is critical for multivariate adjustment of confounders and valid causal inference. Another major unsolved issue is how to combine heterogeneous information content from various health care databases in order to maximize confounding control. This proposal seeks to advance methods for pooling heterogeneous individuallevel electronic healthcare data in order to allow for pooled analyses with full multivariate adjustment with no sharing of private patient data. --- We will develop and test methods and algorithms for pooling both like and heterogeneous data elements, combining claims information from multiple health care providers and then augmenting these claims with electronic medical record and laboratory values. The methods will be neutral with respect to coding standards and will be robust to heterogeneous database structures. --- We will use the methods to perform three example studies, each of which requires pooled data due to infrequent exposure, small patient subgroups, rare outcomes, or a combination of these: (1) effectiveness of high- versus low-potency statin use after acute coronary syndrome with respect to myocardial infarction and cardiovascular death; (2) effectiveness of TNF inhibitors in patients with rheumatoid arthritis with respect to reducing pain medication use and improvement in lab values and X-ray diagnostics; (3) reduced effectiveness of clopidogrel in the presence of proton pump inhibitors in patients with acute coronary syndrome and/or percutaneous coronary intervention, potentially leading to increased re-infarction rates and death versus clopidogrel alone. --- We will explore, both statistically and operationally, when pooling of individual-level data will out-perform aggregate-level meta-analysis, and test the hypothesis that aggregate-level meta-analysis will yield substantially similar point estimates in most scenarios. --- We will publish and provide SAS code for all methods developed, and provide training sessions to relevant groups of researchers in order to broaden the scope and ensure lasting impact of the work performed. This 2-year project will significantly advance methodology for pooling individual-level information from diverse health care databases. The work will allow for comparative effectiveness and safety analyses that is of highpriority for payers (Medicare) and regulators (FDA) and will provide multivariate-adjusted results with no threat to patient privacy. The focus is on broad and expedited practical applicability.