Do Bayesian Adaptive Trials Offer Advantages for CER?

贝叶斯自适应试验是否为 CER 提供优势？

• 批准号: 8036399
• 负责人: C. DANIEL MULLINS
• 金额: $ 149.99万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-20 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8036399
• 关键词: Address; Antihypertensive Agents; Berry; Cardiovascular Diseases; Cardiovascular system; Case Study; Clinical; Clinical Trials; Clinical Trials Design; Collaborations; Combined Modality Therapy; Data; Data Collection; Decision Making; Drops; Event; Evidence Based Medicine; Feedback; Future; Generations; Goals; Health; Healthcare Systems; Institute of Medicine (U.S.); Knowledge; Lead; Learning; Marketing; Measures; Medical; Medical Device; Methodology; Methods; Nature; Outcome; Patient Care; Patients; Performance; Policies; Population Heterogeneity; Principal Investigator; Randomized; Randomized Clinical Trials; Reporting; Research; Research Infrastructure; Risk; Safety; Sample Size; Series; Simulate; Source; Specific qualifier value; Sum; Testing; Time; Uncertainty; United States; active comparator; alternative treatment; base; clinical practice; clinically relevant; comparative effectiveness; comparative trial; cost; design; effectiveness research; experience; improved; innovation; insight; patient oriented; prevent; prospective; public health relevance; simulation; standard of care; statistics; systematic review; treatment strategy

DESCRIPTION (provided by applicant): We propose an innovative approach to designing and conducting randomized clinical trials (RCTs) for comparative effectiveness research (CER). Evidence from "pragmatic" RCTs can inform real-world decisions by comparing clinically relevant alternatives across a wide array of patient-centric outcomes in typical patient care settings serving diverse populations. Unfortunately, they typically increase the time and cost compared to the "explanatory" trials used to gain marketing approval. One example of a well designed and executed active comparator trial was the ALLHAT study, which cost over $130 million and took more than five years to complete. ALLHAT was very informative, yet raises the question of whether future trials of this nature could be designed more efficiently. Without alterations to how trials in the United States are conceived, designed, conducted, and analyzed, the nation risks spending large sums of money answering questions that are less impactful on clinical and policy decision making due to delayed reporting or incomplete comparisons. Bayesian adaptive RCT designs can reduce the sample size, time, and cost of obtaining decision-relevant information by formally incorporating external evidence and incorporating prospectively defined adaptations in order to focus the data collection on the primary aims. Despite successful use in regulatory trials, Bayesian adaptive methods have not yet been rigorously applied to pragmatic CER trials. We believe that exploring their value through a "proof of concept" will enhance scientific knowledge and help overcome the methodological inertia that tends to prevent the use of innovative approaches. Aim 1 is to demonstrate the applicability of Bayesian adaptive clinical trial design methods for CER by developing a series of 18 potential re-designs of the ALLHAT study. This aim includes a systematic review of evidence that was available prior to when ALLHAT was designed to inform the generation of "priors," which reflect probabilistic representations of what was known or hypothesized regarding antihypertensive treatments and includes designing adaptive features. Aim 2 is to evaluate the efficiency and performance of Bayesian adaptive designs through simulation using actual ALLHAT patient data. Aim 3 is to develop a clinically-motivated decision theoretic approach to Bayesian adaptive designs to address additional relevant research questions. The result will be a demonstration of the benefits - as well as some of the trade-offs - of a Bayesian adaptive approach to CER trial design and analysis. PUBLIC HEALTH RELEVANCE: CER is intended to help patients, prescribers, and other medical decision makers select the most appropriate treatments. Traditional clinical trials have strong internal validity, but may not be ideal for decision makers considering alternative treatments that were not "standard of care" when the clinical trial was designed. Bayesian adaptive clinical trial designs introduce efficiencies that retain internal validity while enabling active comparative trials to capture relevant and timely evidence to improve decision making that significantly impacts patients' health and safety.

描述（由申请人提供）：我们提出了一种创新的方法，用于设计和进行比较有效性研究（CER）的随机临床试验（RCT）。来自“务实” RCT的证据可以通过比较典型的患者护理环境中各种以患者为中心的结果的临床相关替代方案来为现实世界的决定提供信息。不幸的是，与用于获得营销批准的“解释性”试验相比，它们通常会增加时间和成本。精心设计和执行的主动比较试验的一个例子是Allhat研究，其成本超过1.3亿美元，花了五年多的时间才能完成。 Allhat非常有用，但提出了一个问题，即是否可以更有效地设计这种性质的试验。如果没有改变对美国试验的构想，设计，进行，进行和分析的方式，该国可能会花费大量资金来回答问题，而这些问题对由于报告延迟或不完整的比较而对临床和政策决策的影响较小。贝叶斯自适应RCT设计可以通过正式合并外部证据并结合前瞻性定义的适应性来减少样本量，时间和成本，以便将数据收集集中在主要目的上。尽管在监管试验中成功使用，但贝叶斯自适应方法尚未被严格应用于实用CER试验。我们认为，通过“概念证明”探索其价值将增强科学知识，并有助于克服倾向于防止使用创新方法的方法论惯性。 AIM 1是通过开发Allhat研究的一系列18个潜在的重新设计来证明贝叶斯自适应临床试验设计方法的适用性。该目的包括对Allhat旨在告知“先验”的生成之前可获得的证据的系统审查，该证据反映了对降压治疗的已知或假设的概率表示，并包括设计适应性特征。目标2是通过使用实际的Allhat患者数据模拟来评估贝叶斯自适应设计的效率和性能。目标3是为贝叶斯自适应设计开发一种临床动机的决策理论方法，以解决其他相关的研究问题。结果将证明贝叶斯自适应方法对CER试验设计和分析的好处以及一些权衡。 公共卫生相关性：CER旨在帮助患者，处方者和其他医疗决策者选择最合适的治疗方法。传统的临床试验具有强大的内部有效性，但对于设计临床试验时不是“标准护理”的替代治疗的决策者来说，可能不是理想的选择。贝叶斯自适应临床试验设计提出了保留内部有效性的效率，同时可以主动进行比较试验，以捕获相关和及时的证据，以改善决策，从而对患者的健康和安全产生重大影响。

项目成果

Do Bayesian adaptive trials offer advantages for comparative effectiveness research? Protocol for the RE-ADAPT study.

• DOI: 10.1177/1740774513497293
• 发表时间: 2013-10
• 期刊: Clinical trials (London, England)
• 作者: Connor JT;Luce BR;Broglio KR;Ishak KJ;Mullins CD;Vanness DJ;Fleurence R;Saunders E;Davis BR
• 通讯作者: Davis BR