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方法来设计和进行随机临床试验（RCT）以进行比较有效性研究（CER）。来自“务实”随机对照试验的证据可以通过比较服务于不同人群的典型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，为现实世界的决策提供信息。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。 ALLHAT 研究是精心设计和执行的主动比较试验的一个例子，该研究耗资超过 1.3 亿美元，历时五年多才完成。 ALLHAT 提供的信息非常丰富，但也提出了这样的问题：未来是否可以更有效地设计此类性质的试验。如果不改变美国试验的构思、设计、实施和分析方式，美国就有可能花费大量资金来回答由于延迟报告或不完整比较而对临床和政策决策影响较小的问题。贝叶斯自适应随机对照试验设计可以通过正式纳入外部证据并纳入前瞻性定义的适应措施，从而将数据收集集中在主要目标上，从而减少获取决策​​相关信息的样本量、时间和成本。尽管贝叶斯自适应方法在监管试验中得到了成功应用，但尚未严格应用于实用的 CER 试验。我们相信，通过“概念验证”探索其价值将增强科学知识，并有助于克服往往阻碍创新方法使用的方法论惯性。目标 1 是通过对 ALLHAT 研究进行一系列 18 项潜在的重新设计，证明贝叶斯自适应临床试验设计方法对 CER 的适用性。这一目标包括对 ALLHAT 设计之前可用的证据进行系统审查，以告知“先验”的生成，这些“先验”反映了抗高血压治疗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 ALLHAT 患者数据进行模拟来评估贝叶斯自适应设计的效率和性能。目标 3 是开发一种基于临床的贝叶斯自适应设计决策理论方法，以解决其他相关研究问题。结果将展示用于 CER 试验设计和分析的贝叶斯自适应方法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很强的内部效度，但对于在设计临床试验时考虑非“护理标准”的替代疗法的决策者来说可能并不理想。贝叶斯自适应临床试验设计提高了保留内部有效性的效率，同时使主动比较试验能够捕获相关且及时的证据，以改进对患者健康和安全产生重大影响的决策。

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