A simulation study comparing slope model with mixed-model repeated measure to assess cognitive data in clinical trials of Alzheimer's disease

Introduction: In clinical trials of Alzheimer's disease, a mixed-model repeated measure approach often serves as the primary analysis when evaluating disease progression; a slope model may be secondary. Methods: Longitudinal change from baseline (14-item version of Alzheimer's Disease Assessment Scale–Cognitive Subscale) was simulated for treatment/placebo from multivariate normal distributions with the variance-covariance matrix estimated from solanezumab trial data. Type I error, power, and bias were based on 18-month treatment contrast. Sample sizes included 500 and 1000 patients/arm. Results: The slope model was more powerful in most scenarios. Mixed-model repeated measure was relatively unbiased in parameter estimation. The slope model yielded unbiased estimates whenever the underlying trajectory was not detectably different from linear. Both methods led to similar type I error. Discussion: In clinical trials of Alzheimer's disease, mixed-model repeated measure analysis with relaxed assumptions on disease progression seems to be preferred. The slope model might be more powerful if the trajectory has little departure from linearity.