Alzheimer's Disease: New Trial Designs for Emerging Challenges

项目摘要/摘要 阿尔茨海默病(AD)的特点是性别、种族、载脂蛋白ε4状态和tau的异质性。APOEε4 在临床试验(例如阿杜卡努单抗试验)中，状态已被用于对AD患者进行分层。患有老年痴呆症的女性 从纵向研究来看，与阿尔茨海默病患者相比，男性的认知能力下降更快。非洲裔美国人的衰落 在记忆测试和视觉空间功能上比白人更快。此外，我们知道，这些变化 认知功能与tau水平呈负相关。除了异构性之外，另一个 AD试验面临的挑战是研究设计中的随访时间。在最近的阿杜卡努单抗试验中， 在研究过程中，对于apoEε4阳性患者，阿杜卡努单抗增加，但他们的随访 《时代》杂志保持不变。拟议的项目将响应PAR-19-070：关于#年当前主题的研究 阿尔茨海默病及其相关痴呆症。我们将开发适应性设计，以允许Modifi阳离子 在AIM中改变剂量的患者的随访时间1.来自aducanumab试验的公布结果 将在模拟研究中用于比较所提议的自适应设计的统计性能 与现有的设计没有后续的时间变化。我们的模拟结果表明，我们提出的 自适应设计保证了I类误码率和功率，而现有设计不能。在目标2中， 我们将使用基线数据和 随时间变化的速度，以更好地了解认知的差异和下降的轨迹。我们 将根据性别、种族、载脂蛋白ε4状态为每个亚群制定一个最佳综合得分，以及 基于ADNI研究的数据。我们将演示如何最好地以统计方式管理这些影响 人口学、遗传学和生物标志物因素对AD患者认知能力的影响。最优复合材料 与我们传统的测量方法相比，分数有望更敏感地检测认知变化 使用。2019年，食品和药物管理局发布了关于制定浓缩战略的fiNAL指南 在临床研究中促进药物开发的创新。治疗效果存在异质性 患者之间有不同的特点。确定更有可能对 在给定的剂量下进行新的治疗将显著提高AD试验的成功率并避免类型fi 阿杜卡努单抗试验中发生的问题。AD试验的自适应浓缩设计将测量 在基于无效的停药的中期分析中，每个亚群的治疗效果，并可以 与现有设计相比，节省了样品大小。我们将增加一个对“错误”的概率的限制 停止治疗无效，以避免停止登记，以便对亚群进行可能有效的治疗。 该项目将为AD研究提供新的统计工具，以便fi科学地识别AD风险个人 并针对不同特征的AD患者快速检测病情进展。

Project Summary/Abstract Alzheimer’s disease (AD) is characterized by heterogeneity in sex, race, APOE ε4 status, and tau. APOE ε4 status has been used to stratify AD patients in clinical trials (e.g., the aducanumab trial). Women with AD have faster cognitive decline compared to men with AD from longitudinal studies. African Americans decline faster than Whites on memory tests and visuospatial functioning. Furthermore, we know that the changes in cognitive functions are negatively associated with tau levels. In addition to the heterogeneity, another challenge facing AD trials is the follow-up time in study designs. In the recent aducanumab trial, the dose of aducanumab was increased during the course of the study for APOE ε4 positive patients, but their follow-up times remained unchanged. The proposed project will respond to PAR-19-070: Research on Current Topics in Alzheimer’s Disease and Its Related Dementias. We will develop adaptive designs to allow the modification of follow-up time for patients with dose change in Aim 1. The published results from the aducanumab trial will be used in simulation studies to compare the statistical performance of the proposed adaptive designs with the existing designs without follow-up time change. Our simulation results indicated that our proposed adaptive designs guarantee the type I error rate and power, while the existing designs do not. In Aim 2, we will develop new optimal composite scores for each subpopulation by using the baseline data and the rate of change over time to better understand the differences in cognition and trajectories of decline. We will develop one optimal composite score for each subpopulation stratified by sex, race, APOE ε4 status, and tau, based on data from the ADNI study. We will demonstrate how best to manage statistically the effects of demographic, genetic, and biomarker factors on cognitive ability of AD patients. The optimal composite scores are expected to be more sensitive to detect cognition change compared to the measures we traditionally use. In 2019, the Food and Drug Administration released a final guidance on developing enrichment strategies in clinical investigations to promote innovation in drug development. Treatment effect heterogeneity exists among patients with different characteristics. Identifying subpopulations who are more likely to respond to a new treatment at a given dose would significantly increase the success rate of AD trials and avoid the types of issues that occurred in the aducanumab trial. Adaptive enrichment designs for AD trials will measure the treatment effectiveness of each subpopulation at the interim analysis for futility based stopping, and can save sample sizes compared to the existing designs. We will add a constraint on the probability of ‘wrong’ stopping for futility to avoid stopping the enrollment for a possible effective treatment on a subpopulation. This project will provide new statistical tools for AD research to efficiently identify individuals at risk of AD and quickly detect disease progression for AD patients with different characteristics.