Alzheimer's Disease: New Trial Designs for Emerging Challenges

项目概要/摘要 阿尔茨海默病 (AD) 的特点是性别、种族、APOE ε4 状态和 tau 蛋白的异质性。载脂蛋白ε4 状态已用于在临床试验中对 AD 患者进行分层（例如，aducanumab 试验）。患有 AD 的女性 根据纵向研究，与患有 AD 的男性相比，认知能力下降得更快。非裔美国人衰落 在记忆测试和视觉空间功能方面比白人更快。此外，我们知道这些变化 认知功能中的 tau 蛋白水平呈负相关。除了异质性之外，还有一个 AD 试验面临的挑战是研究设计的随访时间。在最近的 aducanumab 试验中，剂量 APOE ε4 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。拟议项目将响应 PAR-19-070：当前主题研究 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修改 目标 1 中剂量变化的患者的随访时间。aducanumab 试验公布的结果 将用于模拟研究，以比较所提出的自适应设计的统计性能 与现有设计无后续时间变化。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，而现有设计则不能。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，以更好地了解认知差异和衰退轨迹。我们 将为按性别、种族、APOE ε4 状态分层的每个亚群制定一个最佳综合评分，以及 tau，基于 ADNI 研究的数据。我们将演示如何最好地管理统计影响 人口统计、遗传和生物标志物因素对 AD 患者认知能力的影响。最优复合 与我们传统的测量方法相比，分数预计对检测认知变化更加敏感 使用。 2019年，美国食品药品监督管理局发布了制定浓缩策略的最终指南 临床研究以促进药物开发创新。治疗效果存在异质性 在具有不同特征的患者中。确定更有可能做出反应的亚人群 给定剂量的新疗法将显着提高 AD 试验的成功率并避免以下类型 aducanumab 试验中出现的问题。 AD 试验的自适应富集设计将测量 基于无效停止的中期分析中每个亚群的治疗效果，并且可以 与现有设计相比节省样本量。我们将对“错误”的概率添加一个约束 因徒劳而停止，以避免停止对亚人群可能有效的治疗的招募。 该项目将为AD研究提供新的统计工具，以有效识别有AD风险的个体 快速检测具有不同特征的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.