Harnessing greater statistical power: Comprehensive evaluation of disease modifying treatment effects across all or multiple post-baseline visits compared to the last visit for Alzheimer's disease clinical trials

Abstract

Background

In Alzheimer's disease (AD) clinical trials, efficacy inference is traditionally based on the last visit (e.g., 18 months). However, recent studies suggest that disease-modifying treatment effects may emerge as early as 3 months post-baseline.

Objective

To explore this further, our study aimed to assess the increased statistical power achieved by incorporating all or multiple post-baseline visits to estimate treatment effect, compared to relying solely on the last visit.

Methods

We developed explicit formulas for the basis functions of the natural cubic spline model, ensuring compatibility with standard SAS procedures. Through simulations using disease progression trajectories from Clarity-AD and TRAILBLAZER-ALZ 2 trials, we comprehensively evaluated various models in terms of power and type I error. Additionally, we offer SAS codes that to facilitate seamless implementation of different modeling approaches.

Results

Simulations based on ClarityAD and TRAILBLAZER-ALZ 2 disease trajectories demonstrated that models incorporating multiple or all post-baseline visits yield greater power than those using only the last visit, while maintaining Type I error control. Furthermore, when three post-baseline visits were included, adding more visits resulted in minimal power gains.

Conclusions

Our findings support prioritizing statistical models that incorporate multiple or all post-baseline visits for treatment efficacy inference, as they offer greater efficiency than models relying solely on the last visit.