MARGO: Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights.

ABSTRACT: Adaptive randomization is a promising approach in clinical trials that aims to optimize patient outcomes by adjusting treatment allocation probabilities based on accumulating data. However, implementing adaptive randomization in group sequential trials, which include interim analyses for early stopping decisions, poses significant challenges, such as managing type I error inflation and ensuring robust statistical validity. This paper proposes Machine learning-assisted Adaptive Randomization for Group sequential trials based on Overlap weights (MARGO) as an innovative solution to these challenges. MARGO integrates machine learning (ML) models into the adaptive randomization process, allowing dynamic updates to randomization probabilities based on real-time predictions of treatment success.

SUBMITTER: Park Y 

PROVIDER: S-EPMC12261390 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature