Statistical methods for enriched clinical trials with applications to Alzheimer's disease research

丰富临床试验的统计方法及其在阿尔茨海默病研究中的应用

• 批准号: 10607649
• 负责人: NAVNEET RAM HAKHU
• 金额: $ 3.86万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2025-05-24
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607649
• 关键词: Acceleration; Activities of Daily Living; Address; Alzheimer disease prevention; Alzheimer' s Disease; Attention; Biological Markers; Calibration; Characteristics; Clinical Trials; Clinical Trials Design; Complex; Computer software; Coupled; Data; Dementia; Disease; Eligibility Determination; Ensure; Event; Heterogeneity; Industry; Intervention; Measures; Medical; Methods; Odds Ratio; Outcome; Outcome Measure; Patients; Peer Review; Phase; Prevalence; Publications; Randomized; Randomized Controlled Clinical Trials; Research; Research Personnel; Resources; Sampling; Specific qualifier value; Statistical Methods; Structure; Target Populations; Testing; Time; Time trend; analytical method; comparative; design; drug development; effective therapy; health disparity; improved; ineffective therapies; innovation; interest; mild cognitive impairment; novel; panacea; patient population; patient subsets; precision medicine; primary outcome; prognostic; recruit; response; simulation; symposium; theories; tool; treatment effect; trend; user friendly software; user-friendly

PROJECT SUMMARY / ABSTRACT With the rising prevalence of Alzheimer’s disease (AD) in the U.S. and worldwide, there is a crucial need for preventative and disease-modifying treatments. Randomized controlled clinical trials (RCTs) serve as the gold standard to determine whether a candidate treatment has a favorable benefit-to-risk ratio for a pre-specified target patient population. However, heterogeneity of treatment effects across subpopulations (e.g., due to health disparities) may yield medical interventions that are not one-size-fits-all. Enrichment strategies are commonly employed in RCTs to identify the target populations most likely to benefit from a candidate treatment and/or have the outcome of interest during the course of the trial. Enrichment in AD RCTs aligns with the National Plan to Address AD Strategy 1.B to expand research to develop disease-modifying treatments. Currently, there is a gap in the understanding of RCTs using enrichment and adaptations to the randomized treatment assignment allocations (response-adaptive enrichment), especially for RCTs with a repeated measures (longitudinal, e.g., changes in activities of daily living scores) or censored (time-to-event, e.g., time to dementia) primary outcome. Application of standard statistical methods to enrichment designs may, however, result in bias (tendency to systematically over- or under- estimate treatment effects). Biased estimates can lead to approval of less effective therapies, in the best case, and approval of potentially harmful or ineffective therapies or missing an effective therapy, in the worst case, as a consequence of over- or under- estimating treatment effects. Our conjecture is that the bias induced in a fixed enrichment pre-post (only two assessments; one pre- and one post-randomization) RCT will be exacerbated when using response-adaptive enrichment in longitudinal or time-to-event RCTs. The applicant’s long-term objective as a collaborator on RCTs and independent researcher is to provide well-calibrated and valid statistical inference for complex innovative designs to facilitate drug development in AD and other diseases. This F31 proposal aims to quantify the impact of enrichment (e.g., on bias), and as needed, develop novel statistical methods to obtain valid inference in enriched RCTs with a longitudinal primary outcome (Aim 1) and a time-to-event primary outcome (Aim 2). Simulation studies using data from completed, large phase 3 NIA- and industry-sponsored mild cognitive impairment and AD trials will be used to empirically validate the newly developed theory and methods in real-world settings. To provide resources for trialists, freely-available and user-friendly software based on Aims 1-2 will be developed (Aim 3) as an extension to the existing RCTdesign (www.rctdesign.org) R package, co-authored by the sponsor of this application. Research findings from Aims 1-2 will be disseminated via conference presentations and peer-reviewed publications. Successful completion of Aims 1-3 will provide a framework and tools for trialists to be well-informed when designing enriched RCTs for any disease.

项目总结/摘要 随着阿尔茨海默病（AD）在美国和世界范围内的患病率上升，迫切需要 预防和改善疾病的治疗。随机对照临床试验（RCT）是 确定候选治疗是否具有有利的获益风险比的标准， 目标患者人群。然而，亚群间治疗效果的异质性（例如，由于 健康差距）可能产生的医疗干预措施不是一刀切的。浓缩战略是 通常用于RCT，以确定最有可能从候选治疗中获益的目标人群 和/或在试验过程中具有感兴趣的结果。AD RCT中的富集与 国家计划解决AD战略1.B，以扩大研究，开发疾病改善治疗。 目前，对随机对照试验的理解存在差距， 治疗分配分配（响应自适应富集），特别是对于重复的RCT 测量（纵向，例如，日常生活活动评分的变化）或删失（至事件发生的时间，例如，时间 痴呆）的主要结果。将标准统计方法应用于富集设计， 然而，这会导致偏倚（倾向于系统性高估或低估治疗效果）。偏置 在最好的情况下，估计可能导致批准不太有效的治疗方法， 或无效治疗或错过有效治疗，在最坏的情况下，由于过度或不足- 估计治疗效果。我们的推测是，在一个固定的富集前后（只有两个）， 评估;随机化前和随机化后各一次）RCT将在使用反应自适应 纵向或至事件发生时间RCT的丰富性。申请人作为合作者的长期目标 随机对照试验和独立研究人员的任务是为复杂的 创新的设计，以促进药物开发的AD和其他疾病。F31提案旨在量化 富集的影响（例如，根据需要，开发新的统计方法，以获得有效的 具有纵向主要结局（目标1）和至事件发生时间主要结局的丰富RCT中的推断 (Aim 2）。使用已完成的大型III期NIA和行业申办的轻度 认知障碍和AD试验将用于实证验证新开发的理论和方法 在现实世界中。为了向试验者提供资源，免费提供和用户友好的软件， 将开发目标1-2（目标3）作为现有RCT设计（www.rctdesign.org）R包的扩展， 由本申请的申办者共同撰写。目标1-2的研究结果将通过 会议报告和同行评审的出版物。成功完成目标1-3将提供 框架和工具，供试验者在为任何疾病设计丰富的RCT时充分知情。