Randomized observation study of biologic therapy for rheumatoid arthritis

描述（由申请人提供）：最近的医疗保健研究和质量机构 (AHRQ) 执行摘要表明，需要更好的系统来确定现有与新型且昂贵的类风湿性关节炎 (RA) 生物药物疗法的相对优点。目前对于如何在临床中使用这些不同的生物疗法还没有明确的范例。鉴于治疗 RA 的疗法多种多样，需要 CCE 研究和生物标志物预测来为生物疗法选择提供基本原理算法。目前 FDA 批准了 8 种（正在研发中的第九种）昂贵的生物疗法来治疗 RA。解决药物治疗的比较和成本效益（CCE）问题的研究受到多种因素的阻碍。一方面，依赖于随机药物试验数据的 CCE 研究无法提供对现实世界医疗保健成本的最佳估计，并且接受生物疗法治疗的 RA 患者的随机研究与观察性研究的 CCE 结果之间存在显着差异。然而，观察性研究中缺乏随机性限制了 CCE 数据的分析。例如，现实世界中患者的经验性和非随机治疗选择因混杂或选择偏倚而变得复杂，这些混杂或选择偏倚会显着影响任何 CCE 分析的结果，包括疾病严重程度、用药依从性和不同治疗类别的受试者分布之间的差异。最后，由于缺乏预测一种疗法与另一种疗法的反应性的生物标志物，RA 的 CCE 研究受到阻碍。 CCE 研究的理想系统将允许捕获随机分配到类似疗法的患者的真实成本。我们提出了一种新方法来解决当前的这些障碍，不仅可以进行 CCE 研究，还可以简化个性化医疗的工作。这一概念的本质是，随机化后，护理的所有其他方面都将由患者及其医生决定，包括有关药物剂量、监测和停药的决定。需要随机化来均匀分配患者并消除偏差，并且需要现实世界的观察来捕获有关实际成本和治疗效果的准确信息。我们将此研究设计称为随机观察。我们计划利用匹兹堡大学医学中心 (UPMC) RA 比较有效性研究 (RACER) 系统进行随机观察研究，以比较不同治疗策略的现实效果，从而克服 RA 中 CCE 研究的障碍。最初，我们将从哈佛大学的合作者以及 UPMC RACER 获得 CCE 数据。 UPMC RACER 系统将利用 UPMC 风湿病专家的大型网络，这些网络已经通过电子病历 (EMR) 系统连接起来； EMR 将用于识别 RA 患者并获取有关治疗、医疗费用和临床实验室数据的信息。结合对哈佛大学 BRASS 登记处跟踪的 1,110 多名 RA 患者的分析，我们将展示 UPMC RACER 系统在分析治疗 RA 的生物疗法中的实用性，并且我们将使用此分析的结果来设计未来的 RA 生物疗法随机观察研究。该项目涉及与哈佛大学和匹兹堡大学的研究人员合作，目标是建立系统，以便有效地对 RA 患者进行现实世界的成本效益研究。我们还将收集生物样本用于机制研究和潜在生物标志物的分析，这样我们还可以提供可能适用于其他研究的数据，这些研究将重点关注为 RA 患者提供个性化医疗的研究。

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.