Simulation-based power analysis could improve the design of clinical trials in Alzheimer's disease

Clinical trials of new treatments in different progressive diseases use power analysis to determine the sample size needed for a trial to obtain a statistically significant estimate for an anticipated treatment effect. In trials with parallel designs, the standard power analysis approach is based on a two-sample t-test. For example, the standard t-test approach was used in determining the sample size for the Phase 3 trials of aducanumab, the first drug approved by the United States Food and Drug Administration (FDA) to potentially slow cognitive decline in early-stage Alzheimer's disease. However, t-tests contain normality assumptions, and t-test-based power analyses do not implicitly factor in the uncertainty about anticipated treatment effects that arises due to inter-subject heterogeneity in disease progression. These limitations may lead to recommended sample sizes that are too small, potentially making a trial blind to a treatment effect that is truly present if the cohort's endpoints are not normally distributed and/or the anticipated treatment effect is overestimated. To address these issues, we present a novel power analysis method that (1) simulates clinical trials in a progressive disease using real-world data, (2) accounts for inter-subject heterogeneity in disease progression, and (3) does not depend on normality assumptions. As a showcase example, we used our method to calculate power for a range of sample sizes and treatment effects in simulated trials similar to the Phase 3 aducanumab trials EMERGE and ENGAGE. As expected, our results show that power increases with number of subjects and treatment effect (here defined as the cohort-level percent reduction in the rate of cognitive decline in treated subjects vs. controls). However, inclusion of realistic inter-subject heterogeneity in cognitive decline trajectories leads to increased sample size recommendations compared to a standard t-test power analysis. These results suggest that the sample sizes recommended by the t-test power analyses in the EMERGE and ENGAGE Statistical Analysis Plans were possibly too small to ensure a high probability of detecting the anticipated treatment effect. Insufficient sample sizes could partly explain the statistically significant effect of aducanumab being detected only in EMERGE. We also used our method to analyze power in simulated trials similar the Phase 3 lecanemab trial Clarity AD. Our results suggest that Clarity AD was adequately powered, and that power may be influenced by a trial's number of analysis visits and the characteristics of subgroups within a cohort. By using our simulation-based power analysis approach, clinical trials of treatments in Alzheimer's disease and potentially in other progressive diseases could obtain sample size recommendations that account for heterogeneity in disease progression and uncertainty in anticipated treatment effects. Our approach avoids the limitations of t-tests and thus could help ensure that clinical trials are more adequately powered to detect the treatment effects they seek to measure.