Predicted natural progression as an Alzheimer's prognostic covariate improves the precision of lecanemab efficacy assessments and clinical trial efficiency.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

DISCUSSION

APCs enhance treatment effect evaluation, improve statistical power, and reduce required sample sizes in Alzheimer's trials.

GOV IDENTIFIERS

NCT01767311 (Lecanemab Study 201), NCT03887455 (Lecanemab Study 301; ClarityAD).

HIGHLIGHTS

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.