Alzheimer's Disease: New Trial Designs for Emerging Challenges

项目总结/摘要 阿尔茨海默病（AD）的特征在于性别、种族、APOE ε 4状态和tau的异质性。APOE ε 4 状态已被用于在临床试验中对AD患者进行分层（例如，aducanumab试验）。女性AD患者 与纵向研究中的AD男性相比，非洲裔美国人减少 在记忆测试和视觉空间功能上比白人快此外，我们知道， 与tau蛋白水平呈负相关。除了异质性，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aducanumab试验中， 在APOE ε 4阳性患者的研究过程中，aducanumab增加，但他们的随访 时间保持不变。拟议的项目将响应PAR-19 - 070：对当前主题的研究， 阿尔茨海默病及其相关痴呆症。我们将开发自适应设计， 目标1中剂量改变患者的随访时间。Aducanumab试验的已发表结果 将用于模拟研究，以比较所提出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类错误率和功率，而现有设计不能。在目标2中， 我们将使用基线数据和 随着时间的推移变化率，以更好地了解认知和下降轨迹的差异。我们 将为按性别、种族、APOE ε 4状态分层的每个亚群制定一个最佳综合评分， 根据ADNI研究的数据。我们将展示如何最好地管理统计的影响， 人口统计学、遗传学和生物标志物因素对AD患者认知能力的影响。最佳组合 与我们传统的测量方法相比， 使用. 2019年，美国食品药品监督管理局发布了关于制定浓缩战略的最终指南 临床研究，以促进药物开发的创新。治疗效果存在异质性 不同特征的患者。确定哪些亚群更有可能对 在给定剂量下的新治疗方法将显著提高AD试验的成功率，并避免 aducanumab试验中出现的问题。AD试验的适应性富集设计将测量 在中期分析时，每个亚群对基于无效的停药的治疗有效性，并且可以 与现有设计相比，节省了样本量。我们将增加一个约束的概率'错' 因无效而停止，以避免停止入组亚群的可能有效治疗。 该项目将为AD研究提供新的统计工具，以有效识别AD风险个体 快速检测不同特征AD患者的疾病进展。

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.