Estimating Correlations Between Clinical Trial Outcomes Using Generalised Estimating Equations

ABSTRACT Accurately estimating the correlations among clinical trial outcomes is crucial for managing the risk of biopharmaceutical investment portfolios. We propose a novel algorithm for estimating correlations in large clinical trial datasets using a generalised estimating equations (GEE) framework. Our algorithm outperforms existing methods in both convergence speed and computational efficiency. Empirical analysis of over 25,000 clinical trials reveals a correlation of approximately 10% for trials within therapeutic areas and up to 40% for trials sharing the same phase or mechanism hierarchy. Trials targeting the same disease show slightly negative correlations, suggesting a first‐mover advantage. Our approach offers a scalable method to estimate correlations within large clinical trial datasets.