Work

Estimating Correlations Between Clinical Trial Outcomes Using Generalised Estimating Equations

Year: 2025

Type: article

Abstract: 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.

Source: Oxford Bulletin of Economics and Statistics

DOI: 10.1111/obes.70025

Authors: Yuehao Dai, Andrew W. Lo, Manish Singh, Qingyang Xu, Ruixun Zhang

Institutions: Peking University, Santa Fe Institute, MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,840