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.

项目摘要/摘要 随着阿尔茨海默病(AD)在美国和世界范围内的流行，迫切需要 预防性和改良性治疗。随机对照临床试验(RCT)是金 标准，以确定候选治疗是否对预先指定的 目标患者群体。然而，各亚群之间的治疗效果的异质性(例如，由于 健康差距)可能会产生不是一刀切的医疗干预措施。浓缩战略是 通常用于随机对照试验，以确定最有可能从候选治疗中受益的目标人群 和/或在试验过程中具有感兴趣的结果。AD RCT中的浓缩与 应对AD战略1.B的国家计划，以扩大研究以开发疾病修正疗法。 目前，人们对随机对照试验的理解存在差距，采用的是丰富和适应随机对照试验的方法 治疗分配分配(反应-适应性浓缩)，特别是对重复的随机对照试验 测量(纵向的，例如日常生活活动得分的变化)或审查(事件发生的时间，例如时间 痴呆症)主要结果。将标准统计方法应用于丰富设计可以， 然而，这会导致偏差(系统地高估或低估治疗效果的倾向)。有偏见的 在最好的情况下，评估可能会导致批准效果较差的治疗方法，以及批准可能有害的治疗方法 或无效的治疗或错过有效的治疗，在最坏的情况下，由于过度或不足- 评估治疗效果。我们的猜测是，在固定的浓缩前(只有两个)产生的偏差 评估；一次随机化前和一次随机化后)在使用响应自适应时将加剧RCT 纵向或事件发生时间随机对照试验中的浓缩。申请人作为合作者的长期目标是 RCTS和独立研究人员将为复杂情况提供经过良好校准和有效的统计推断 创新设计，促进AD和其他疾病的药物开发。这份F31提案旨在量化 丰富的影响(例如，对偏见)，并根据需要开发新的统计方法，以获得有效的 具有纵向主要结果(目标1)和事件发生时间主要结果的强化随机对照试验的推断 (目标2)。使用NIA和行业赞助的已完成的大型第三阶段MIRED数据进行的模拟研究 认知障碍和AD试验将被用来对新开发的理论和方法进行实证验证 在真实世界的环境中。为试用者提供资源，免费提供和用户友好的软件基于 将开发AIMS 1-2(AIMS 3)，作为现有RCT设计(www.rctsign.org)R包的扩展， 由本申请的发起人共同撰写。AIMS 1-2的研究成果将通过 会议发言和同行评议出版物。成功完成目标1-3将提供 框架和工具，使试验人员在设计针对任何疾病的丰富的随机对照试验时获得充分的信息。