Supervised Machine Learning and Active Learning for Surrogate Outcome Detection in Clinical Protocols

A surrogate outcome is a substitute measure for a patient-final outcome; a direct measure of how a person feels, functions and/or survives. Intermediate outcomes can be defined as standardised functionality measures. Surrogate outcomes are increasingly used in clinical trials to accelerate drug approvals, yet the reporting of their use as primary endpoints in study protocols remains inconsistent. This is an issue because the primary endpoint is used to determine treatment efficacy and thus is crucial for interpreting results and making regulatory decisions. We considered various supervised learning techniques for detecting surrogate outcome usage using a three-class text classification approach. We evaluated this approach on two nervous system trial protocol datasets (EUCT-NS and NS-HRA). This study serves as a proof of concept for the potential of machine learning to automate the detection of primary surrogate endpoint use in clinical trial protocols, thus addressing gaps in reporting.