Devanarayan Viswanath, Ye Yuanqing, Zhu Liang, Tian Lu, Kramer Lynn, Irizarry Michael, Dhadda Shobha
Clinical Evidence Generation, Eisai Inc., Nutley, New Jersey, USA.
Department of Mathematics, Statistics and Computer Science, University of Illinois Chicago, Chicago, Illinois, USA.
Alzheimers Dement. 2025 Mar;21(3):e70045. doi: 10.1002/alz.70045.
Heterogeneity in Alzheimer's disease (AD) progression introduces variability in treatment effect assessments. Using predicted future progression as an AD prognostic covariate (APC) may reduce this variability. This study evaluates this strategy in lecanemab trials and its implications for AD trial design.
Two APCs were derived at baseline for each trial participant from published models with historical controls: one with clinical features, the other adding structural MRI features. Their impact on estimating the difference in cognitive decline between the treatment and placebo arms and the time saved from delayed progression (TSDP) was assessed.
Incorporating either APC reduced variance estimates by up to 19.1% across phase II and phase III trials, increased power to 90.2%, and reduced sample size by 27.2%. These APCs improved treatment effect estimates and TSDP, demonstrating broad applicability across endpoints.
APCs enhance treatment effect evaluation, improve statistical power, and reduce required sample sizes in Alzheimer's trials.
NCT01767311 (Lecanemab Study 201), NCT03887455 (Lecanemab Study 301; ClarityAD).
Baseline prediction of future progression can serve as an APC for treatment effect assessments. These predictions can be derived from progression models developed using external controls. APC accounts for heterogeneity in progression among trial participants, improving treatment effect estimates. Enhanced accuracy and precision were observed across lecanemab phase II and phase III trials for various endpoints. This approach results in substantial increase in statistical power and reduced sample size for future AD trials.
阿尔茨海默病（AD）进展的异质性导致治疗效果评估存在变异性。将预测的未来进展用作AD预后协变量（APC）可能会减少这种变异性。本研究在lecanemab试验中评估了这一策略及其对AD试验设计的影响。
利用具有历史对照的已发表模型，在基线时为每个试验参与者得出两个APC：一个基于临床特征，另一个增加了结构MRI特征。评估了它们对估计治疗组和安慰剂组认知衰退差异以及延缓进展节省时间（TSDP）的影响。
在II期和III期试验中，纳入任一APC均可使方差估计值降低多达19.1%，检验效能提高到90.2%，样本量减少27.2%。这些APC改善了治疗效果估计和TSDP，表明在各个终点均具有广泛适用性。
APC增强了AD试验中的治疗效果评估，提高了统计效能，并减少了所需样本量。
NCT01767311（Lecanemab研究201），NCT03887455（Lecanemab研究301；ClarityAD）。
未来进展的基线预测可作为治疗效果评估的APC。这些预测可从使用外部对照开发的进展模型中得出。APC考虑了试验参与者进展的异质性，改善了治疗效果估计。在lecanemab II期和III期试验的各个终点均观察到准确性和精确性提高。这种方法可大幅提高未来AD试验的统计效能并减少样本量。
