Using Outcomes Analyses to Inform Implementation of NINDS Clinical Trials

随机对照试验（RCT）结果缓慢地扩散到临床实践中-从试验完成到广泛采用新治疗的平均时间接近20年。这些延迟导致神经系统疾病患者的治疗效果不佳。鉴于这些延迟和NINDS临床试验结果的巨大社会价值，NINDS认识到需要通过促进研究将试验结果转化为常规护理（T2转化研究）来加速实施。该应用程序旨在通过个性化临床试验结果和解决临床医生和政策制定者的翻译障碍来优化NINDS试验的翻译。使用转化研究方法，我们可以通过估计个体患者的治疗益处，从一刀切的循证医学转向个性化医学。其他翻译方法可以评估和解决阻碍翻译的利益相关者问题。由于临床医生往往对试验结果持怀疑态度，因此改变实践需要说服他们，不仅要让他们相信一种治疗方法在RCT中有效，或者在学术医疗中心有效，而且要让他们相信这种治疗方法对他们的患者有效。同样，如果政策制定者和支付者能够确信一种新的治疗方法是有价值的（例如，有利的成本效益比），他们可以利用他们对医疗保健提供系统的巨大影响力来促进翻译。具体而言，我们将使用翻译研究方法来解决三个重要问题，以改善审判翻译：1。使用多变量结果预测估计个体水平结果2.使用模拟分析估计真实的世界环境对结果的影响;成本效益分析。这些分析的结果可以直接影响临床医生和政策制定者，或者通过使用工具，如网站和移动的应用程序。这项建议有两个主要目标。首先，我们将通过解决颈动脉血运重建术与支架植入术（CREST）试验中基本的预防相关问题，使转化研究方法适应临床试验。其次，我们将开发一个模型，同时在神经病学紧急治疗试验（NETT）网络中进行类似的翻译分析。这些目标将通过三个具体目标来实现：1.在CREST试验中，使用精确的多变量结局预测方法，估计颈动脉内膜切除术（CEA）与颈动脉支架植入术（CAS）对个体患者的预期净获益。2.为了估计个性化决策和真实的世界环境（例如，不同的并发症发生率）对真实的世界患者的CAS与CEA净获益的影响。3.通过评估新启动和最近完成的NETT试验中的流程实施模型，评估在NETT试验中执行并行转化和成本分析的可行性。Burke博士拥有独特的血管神经学家背景，通过备受推崇的Robert Wood约翰逊临床学者计划接受转化研究方法学培训。Burke博士将在临床试验、多变量结局预测、模拟分析和成本分析方面发展额外的专业知识，成为神经转化研究领域的领导者和独立资助的研究者，致力于开发新一代NETT试验，以更好地为真实的世界临床实践提供有效信息，并改善患者结局。该提案利用了密歇根大学独特的环境优势。最重要的是，Burke博士将得到一个杰出的多学科导师团队的支持，包括NETT临床协调中心（CCC）首席研究员William Barsan博士和神经系统疾病紧急治疗的研究领导者，Rodney海沃德博士是内科教授和转化研究的先驱，刘易斯Morgenstern博士是神经系统转化研究的领导者。这三位导师在指导初级教师和将初级教师转变为独立教师方面都有出色的记录。此外，Burke博士将有机会通过NETT参与独特的实践临床试验沉浸式学习，以获得临床试验设计，管理和实施方面的经验。最后，密歇根大学最近建立了美国最大的学术转化研究中心（卫生政策和创新研究所），该中心将支持先进的统计数据， 本提案所需的方法。

DESCRIPTION (provided by applicant): Randomized controlled trial (RCT) results diffuse into clinical practice slowly - the average time from trial completion to widespread adoption of a new treatment is nearly 20 years. These delays result in suboptimal treatment for patients with neurological diseases. In light of these delays and the enormous societal value of NINDS clinical trials findings, NINDS has recognized the need to accelerate implementation by promoting research to translate trial findings into routine care (T2 translational research). This application seeks to optimize translation of NINDS trials by personalizing clinical trial results ad addressing barriers to translation for clinicians and policy-makers. Using translational research methods, we can move from one-size-fits-all evidence-based medicine towards personalized medicine by estimating treatment benefit for individual patients. Other translational methods can evaluate and address stakeholder concerns that hinder translation. Because clinicians are often skeptical of trial results, changing practice requires convincing them not only that a treatment works in an RCT or that it works in academic medical centers, but that it will work for their patients. Similarly, if policy-makers and payers can be convinced that a new treatment is a good value (e.g., a favorable cost-benefit ratio), they can use their considerable influence on the healthcare delivery system to facilitate translation. Specifically, we will use translational research methods to address three important issues essential to improving trial translation: 1. estimating individual-level outcomes using multivariable outcome prediction 2. Estimating the impact of real world circumstances on outcomes using simulation analyses and 3. Cost effectiveness analysis. Results from these analyses can influence clinicians and policy-makers directly or through the use of tools, such as websites and mobile applications. This proposal has two key objectives. First, we will adapt translational research methods to clinical trials by addressing essential translation-relevant questions for the Carotid Revascularization Endarterectomy versus Stenting (CREST) trial. Second, we will develop a model to concurrently perform similar translational analyses in the Neurology Emergency Treatment Trials (NETT) network. These objectives will be addressed through 3 specific aims: 1. to estimate the expected net benefit of carotid endarterectomy (CEA) vs. carotid artery stenting (CAS) for individual patients in the CREST trial using refined multivariable outcome prediction methods. 2. To estimate the impact of personalized decision-making and real world circumstances (e.g., differing complication rates) on the net benefit of CAS vs. CEA for real world patients using simulation analyses. 3. To assess the feasibility of performing concurrent translational and cost analyses in NETT trials by evaluating a process implementation model in newly initiated and recently completed NETT trials. Dr. Burke has a unique background as a vascular neurologist with training in Translational research methodology through the highly regarded Robert Wood Johnson Clinical Scholars Program. In this proposal, Dr. Burke will develop the additional expertise in clinical trials, multivariable outcome prediction, simulation analyses and cost analyses to become a leader and independently-funded investigator in neurological translational research working to develop a new generation of NETT trials better designed to effectively inform real world clinical practice and improve patient outcomes. This proposal capitalizes on unique environmental strengths at the University of Michigan. Most importantly, Dr. Burke will be supported by an outstanding multi- disciplinary mentorship team including Dr. William Barsan the NETT Clinical Coordinating Center (CCC) principal investigator and a research leader in the emergency treatment of neurological diseases, Dr. Rodney Hayward a Professor of Internal Medicine and a pioneer in translational research and Dr. Lewis Morgenstern, a leader in neurological translational research. All three mentors have excellent track records in mentoring junior faculty and transitioning junior faculty to independence. In addition, Dr. Burke will have te opportunity to participate in a unique hands-on clinical trials immersion through the NETT to gain experience in clinical trial design, management and implementation. Finally, the University of Michigan has recently built the largest academic Translational Research center in the United States (the Institute for Health Policy and Innovation) which will support the advanced statistical methods required for this proposal.