Enhancing early Alzheimer's disease clinical trials through prognostic score covariate adjustment
Bruno T. Scodari, Roland Brown, Xiaotong Jiang, Changyu Shen, Kyle Ferber, Shuang Wu, Feng Gao, Szofia Bullain, Brian MillenAbstract
INTRODUCTION
A prognostic score (PS) summarizes a patient's expected disease progression and can increase the statistical efficiency of clinical trials when included as an analysis covariate.
METHODS
We pooled patient data from observational studies and randomized trials for early Alzheimer's disease (AD) and trained PS candidates to predict 18‐month changes in the Clinical Dementia Rating Scale – Sum of Boxes (CDR‐SB) score. The efficiency gains achieved through covariate adjustment were evaluated in a held‐out trial ( N = 650).
RESULTS
A machine learning PS achieved a Pearson correlation of 0.48 between predicted and observed CDR‐SB changes in an internal test set ( N = 398). Adjusting for this PS in the held‐out trial increased power from 80% to 87.9% (95% confidence interval [CI]: 85.5%–90.2%) with the original sample size. Alternatively, this approach could reduce the required sample size by 19.7% (95% CI: 13.7%–25.7%) while maintaining 80% power.
DISCUSSION
Our findings support the use of PS adjustment for enhancing the efficiency of early AD trials.