Optimizing Binary Endpoint Analysis: A Quantitative Framework for Enhanced Clinical Trial Success

Music City Center 

Binary outcomes are frequently used across various therapeutic areas. Integrating prognostic baseline covariates leads to more robust hypothesis testing. Despite their widespread use, there is no standardized approach across the industry, and different methods are applied with no clear pattern. An assessment of clinical studies revealed diverse methods such as Cochran-Mantel-Haenszel (CMH), Mantel-Haenszel (MH) estimation with Wald test, logistic regression, and Miettinen and Nurminen (MN), among others.
Current literature and FDA guidance do not adequately address the comparative performance of these methods. We aim to enhance our understanding of potential methods for binary data analysis by evaluating their relative efficiency under varied statistical assumptions and clinical settings. Our goal is to develop a quantitative framework to identify the appropriate analysis method(s) that maximize the probability of trial success. This involves considering trial characteristics across therapeutic areas and optimizing method selection for protocol development and regulatory engagement.

Binary endpoints

Cochran-Mantel-Haenszel (CMH)

Mantel-Haenszel (MH) estimation

Miettinen and Nurminen (MN) 

Biopharmaceutical Section