课题基金 / 基金详情

Effectiveness studies with securely pooled healthcare data and adjusted analyses

Effectiveness studies with securely pooled healthcare data and adjusted analyses
通过安全汇总的医疗数据和调整后的分析进行有效性研究
批准号:
7812867
负责人:
Sebastian G. Schneeweiss
金额:
$47.99万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-24 至 2011-08-31

项目摘要

项目成果

Sebastian G. Schneeweiss的其他基金

相似基金

相关文献

中文摘要
翻译
描述(由申请人提供):食品和药物管理局修正案(FDAAA)要求FDA在2012年前获得覆盖1亿人生命的多个分布式大额索赔和电子医疗记录数据库的访问权限。汇集的数据库将提供足够的大小来研究极其罕见的事件,并提供一个与常规护理数据进行广泛比较安全性和有效性研究的平台。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年人口。数据隐私要求将限制患者详细信息的共享,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。另一个尚未解决的主要问题是如何结合来自不同医疗保健数据库的不同信息内容,以便最大限度地进行混淆控制。这项建议旨在推进不同类型的个体级电子医疗保健数据的汇集方法,以便允许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集分析。-我们将开发和测试汇集相似和不同数据元素的方法和算法,合并来自多个医疗保健提供商的索赔信息,然后使用电子医疗记录和实验室价值增强这些索赔。这些方法在编码标准方面将是中立的,并且对不同的数据库结构将是稳健的。-我们将使用这些方法进行三项实例研究,每项研究都需要汇集数据,因为它们不经常暴露、患者分组较少、结果罕见或它们的组合:(1)急性冠脉综合征后使用高效力和低效力他汀类药物对心肌梗死和心血管死亡的有效性;(2)在类风湿关节炎患者中使用肿瘤坏死因子抑制剂在减少止痛药使用、改善实验室数值和X光诊断方面的有效性;(3)在急性冠脉综合征和/或经皮冠状动脉介入治疗的患者中,在质子泵抑制剂存在的情况下,氯吡格雷的有效性降低,与单独使用氯吡格雷相比,可能会导致再梗死率和死亡率增加。-我们将从统计和操作两个方面探索个人层面的数据汇集何时会优于聚合层面的元分析,并测试聚合层面的荟萃分析在大多数情况下将产生基本上相似的点估计的假设。-我们将为所有开发的方法发布和提供SAS代码,并为相关研究人员提供培训课程,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水平信息的方法。这项工作将允许进行对支付者(联邦医疗保险)和监管机构(FDA)高度优先的比较有效性和安全性分析,并将提供不会威胁患者隐私的多变量调整结果。重点是广泛和快速的实用适用性。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
New approaches to safety monitoring of novel systemic treatments for atopic dermatitis in clinical practice and underrepresented populations
  • 批准号:
    10339592
  • 项目类别:
  • 资助金额:
    $76.81万
  • 财政年份:
    2022
  • 负责人:
    Sebastian G. Schneeweiss
  • 依托单位:
New approaches to safety monitoring of novel systemic treatments for atopic dermatitis in clinical practice and underrepresented populations
  • 批准号:
    10559698
  • 项目类别:
  • 资助金额:
    $65.62万
  • 财政年份:
    2022
  • 负责人:
    Sebastian G. Schneeweiss
  • 依托单位:
Randomized Cardiovascular Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology (RCT DUPLICATE)
  • 批准号:
    10606588
  • 项目类别:
  • 资助金额:
    $71.34万
  • 财政年份:
    2019
  • 负责人:
    Sebastian G. Schneeweiss
  • 依托单位:
Randomized Cardiovascular Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology (RCT DUPLICATE)
  • 批准号:
    9898456
  • 项目类别:
  • 资助金额:
    $69.19万
  • 财政年份:
    2019
  • 负责人:
    Sebastian G. Schneeweiss
  • 依托单位:
海外基金