Wu Chao-Yi, Chen Liu, Dickson John R, Zhang Bo, Arnold Steven E, Dodge Hiroko H
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.
Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Alzheimers Dement. 2025 Jul;21(7):e70460. doi: 10.1002/alz.70460.
With the advent of Alzheimer's disease (AD)-modifying and symptomatic treatments of demonstrated efficacy, enrolling participants as concurrent placebo controls in trials can become increasingly difficult. Synthetic controls have been proposed as a viable alternative to concurrent control groups, but their feasibility and reliability remain untested in AD studies.
I-CONECT trial, which evaluates conversational interactions on cognition, was used to test synthetic control methods. Data from the National Alzheimer's Coordinating Center-Uniform Data Set was used to create synthetic-controls for I-CONECT participants using two methods: 1) case mapping and 2) case modeling. Efficacy estimates were compared between original versus synthetic-controlled trials.
In parallel-group designs, treatment effect sizes for the primary outcome were closely aligned between the original trial (β = 1.67) and synthetic control analyses (β = 1.40-1.65). For n-of-1 designs, the two methods showed high agreement in identifying treatment responders (Kappa = 0.75-0.82).
Synthetic control methods are feasible and reliable to create alternative controls in AD studies.
NCT02871921.
Synthetic control methods are feasible and suitable for evaluating treatment effects in various trial designs such as n-of-1, single-arm, and parallel groups. Synthetic control methods can help replicate early-phase Alzheimer's trials, informing go/no-go decisions for larger-scale studies. The choice of similarity algorithms is critical as it affects the quality of historical case mapping. The National Alzheimer's Coordinating Center-Uniform Data Set (NACC-UDS) provided an ideal pool for identifying historical cases with similar demographic, biological, and social characteristics to participants in trials, enabling the creation of synthetic control groups for Alzheimer's clinical research.
随着阿尔茨海默病(AD)改善病情和对症治疗方法疗效的证实,在试验中招募参与者作为同期安慰剂对照变得越来越困难。合成对照已被提议作为同期对照组的可行替代方案,但它们在AD研究中的可行性和可靠性仍未得到检验。
I-CONECT试验评估了认知方面的对话互动,用于测试合成对照方法。使用来自国家阿尔茨海默病协调中心统一数据集的数据,通过两种方法为I-CONECT参与者创建合成对照:1)病例映射和2)病例建模。比较了原始试验与合成对照试验之间的疗效估计。
在平行组设计中,主要结局的治疗效应大小在原始试验(β = 1.67)和合成对照分析(β = 1.40 - 1.65)之间密切一致。对于单病例设计,两种方法在识别治疗反应者方面显示出高度一致性(卡帕值 = 0.75 - 0.82)。
合成对照方法在AD研究中创建替代对照是可行且可靠的。
NCT02871921。
合成对照方法可行且适用于评估各种试验设计(如单病例、单臂和平行组)中的治疗效果。合成对照方法有助于复制早期阿尔茨海默病试验,为大规模研究的决策提供依据。相似性算法的选择至关重要,因为它会影响历史病例映射的质量。国家阿尔茨海默病协调中心统一数据集(NACC-UDS)为识别与试验参与者具有相似人口统计学、生物学和社会特征的历史病例提供了理想资源,从而能够为阿尔茨海默病临床研究创建合成对照组。