Randomized observation study of biologic therapy for rheumatoid arthritis

类风湿性关节炎生物治疗的随机观察研究

• 批准号: 7942942
• 负责人: MARC C. LEVESQUE
• 金额: $ 136.69万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 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 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7942942
• 关键词: Academic Medical Centers; Address; Algorithms; Anti-Tumor Necrosis Factor Therapy; Bioinformatics; Biological; Biological Markers; Biological Response Modifier Therapy; Blood; Caring; Categories; Clinic; Clinical; Collaborations; Communities; Computerized Medical Record; Cost Effectiveness Analysis; Data; Data Collection; Disease; Dose; Effectiveness; FDA approved; Funding; Future; Goals; Hand; Head; Health Care Costs; Healthcare; Hospitals; Immune; Institution; Laboratories; Lead; Link; Logistics; Mediating; Medical; Medical center; Medicine; Methotrexate; Molecular Profiling; Monitor; Observational Study; Outcome; Patients; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacotherapy; Physicians; Random Allocation; Randomized; Registries; Relative (related person); Research; Research Design; Research Personnel; Resources; Rheumatoid Arthritis; Selection Bias; Severity of illness; Specimen; System; Testing; Tumor Necrosis Factor-alpha; Tumor Necrosis Factors; United States Agency for Healthcare Research and Quality; Universities; Woman; World Health; base; comparative; comparative effectiveness; cost; cost effectiveness; design; drug market; effectiveness research; inhibitor/antagonist; medication compliance; novel; novel strategies; patient population; public health relevance; randomized trial; rheumatologist; subcutaneous; therapeutic effectiveness; treatment strategy

DESCRIPTION (provided by applicant): A recent Agency for Healthcare Research and Quality (AHRQ) executive summary indicated that better systems are needed to determine the relative merits of existing versus new and expensive biologic drug therapies for rheumatoid arthritis (RA). There is currently no clear paradigm for how these different biologic therapies should be used in the clinic. CCE studies and biomarker predictions are needed to provide rationale algorithms for biological therapy selection given the extensive array of therapies for treating RA. There are now 8 (a ninth in the pipeline) expensive biological therapies approved by the FDA to treat RA. Research that addresses the comparative and cost effectiveness (CCE) of drug therapies is hindered by several factors. On the one hand, CCE studies that rely on randomized drug trial data do not provide the best estimates of real world health care costs and there is significant disparity between CCE results from randomized versus observational studies in RA patients treated with biologic therapies. However, the absence of randomization in observational studies imposes limitations on the analysis of CCE data. For example, the empiric and non-random selection of therapies for patients in the real world is complicated by confounding or selection bias that significantly impact the outcome of any CCE analysis including differences between groups in disease severity, medication compliance, and subject distribution into different treatment categories. Finally, CCE research in RA is hindered by a lack of biomarkers that predict responsiveness to one therapy versus another. The ideal system for CCE research would allow real world costs to be captured in patients that were randomly assigned to comparable therapies. We propose a Novel approach to address these current obstacles for performing not only CCE research but also streamlining efforts for personalized medicine. Essential to this concept is that following randomization, all other aspects of care would be governed by the patient and their physician, including decisions about drug dosing, monitoring and discontinuation. Randomization is needed to distribute patients evenly and remove biases, and real world observation is needed to capture accurate information on actual costs and therapeutic effectiveness. We refer to this study design as randomized observation. We plan to overcome the barriers to CCE research in RA by utilizing the University of Pittsburgh Medical Center (UPMC) RA Comparative Effectiveness Research (RACER) system to perform randomized observational studies to compare real world effectiveness of different treatment strategies. Initially, we will obtain CCE data from collaborators at Harvard and also from the UPMC RACER. The UPMC RACER system will utilize a large network of UPMC rheumatologists that are already linked by an electronic medical record (EMR) system; the EMR will be used to identify RA patients and to capture information on treatment, medical costs and clinical laboratory data. In conjunction with the analysis of over 1,110 RA patients followed at Harvard in the BRASS registry, we will demonstrate the UPMC RACER system's utility in an analysis of biologic therapies for treating RA and we will use the results of this analysis to design a future randomized observation study of biologic therapy for RA. This project involves a collaboration with researchers at Harvard University and the University of Pittsburgh with the goal of establishing the systems in order to effectively perform real-world cost-effectiveness research in patients with RA. We will also collect biological specimens for analyses of mechanistic studies and potential biomarkers so we can also provide data that potentially will be applicable for additional studies that will focus on research that will lead to personalized medicine for patients with RA.

描述（由申请人提供）：最近的医疗保健研究与质量机构（AHRQ）执行摘要表明，需要更好的系统来确定类风湿关节炎（RA）的现有新且昂贵的生物药物疗法的相对优点。目前尚无明确的范式，即如何在诊所中使用这些不同的生物疗法。需要进行CCE研究和生物标志物预测，以提供用于生物疗法选择的基本原理算法，鉴于用于治疗RA的广泛疗法。现在有8个（管道中的第九位）昂贵的生物疗法由FDA批准用于治疗RA。涉及药物疗法的比较和成本效益（CCE）的研究受到了几个因素的阻碍。一方面，依赖于随机药物试验数据的CCE研究并不能提供对现实世界中医疗保健成本的最佳估计，并且在用生物学治疗治疗的RA患者中，CCE与随机研究与观察性研究之间存在显着差异。但是，观察性研究中没有随机分组对CCE数据的分析施加局限性。例如，现实世界中患者的经验性和非随机疗法选择的混淆或选择偏见使任何CCE分析的结果都变得复杂，包括疾病严重程度，药物依从性和受试者分布在不同治疗类别中的差异。最后，缺乏预测一种对一种疗法的反应性与另一种疗法的反应能力的生物标志物缺乏生物标志物的阻碍。 CCE研究的理想系统将允许在随机分配给可比较疗法的患者中捕获现实的成本。我们提出了一种新型方法，以解决这些当前的障碍，不仅可以进行CCE研究，还可以简化个性化医学的努力。这个概念的必不可少的是，随机分组后，所有其他护理的其他方面都将由患者及其医师支配，包括关于药物给药，监测和停药的决定。需要随机分配均匀分配患者并消除偏见，并且需要进行现实世界的观察以捕获有关实际成本和治疗效果的准确信息。我们将这项研究设计称为随机观察。我们计划通过利用匹兹堡大学医学中心（UPMC）RA比较有效性研究（RACER）系统来克服RA中CCE研究的障碍，以进行随机观察性研究，以比较不同治疗策略的现实世界有效性。最初，我们将从哈佛大学的合作者和UPMC赛车手那里获取CCE数据。 UPMC赛车系统将利用已经通过电子病历（EMR）系统连接的大型UPMC风湿病专家网络； EMR将用于识别RA患者并捕获有关治疗，医疗费用和临床实验室数据的信息。结合对黄铜注册中哈佛的1,110多名RA患者的分析，我们将证明UPMC Racer System在对RA的生物治疗分析中的分析中，我们将使用该分析的结果来设计RA生物学疗法的未来随机观察研究。该项目涉及与哈佛大学和匹兹堡大学的研究人员合作，目的是建立系统，以便在RA患者中有效地进行现实的成本效益研究。我们还将收集生物标本，以分析机械研究和潜在的生物标志物，因此我们还可以提供可能适用于其他研究的数据，该研究将重点放在为RA患者提供个性化医学的研究上。