Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer

• URL: http://arxiv.org/abs/2603.00757v1
• Date: Sat, 28 Feb 2026 18:00:26 GMT
• Title: Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer
• Authors: Adam Marcus, Paul Agapow,
• Abstract summary: Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes.<n>Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials.<n>Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method.<n>Performance was evaluated using synthetic data and applied to clinical trials examining the effectiveness of panitumumab to treat metastatic colorectal cancer.
• Score: 0.45835414225547183
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes. A necessary prerequisite is to identify subgroups of patients who respond differently to different therapies. Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials. Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method. The resulting time-specific responses to treatment are characterised using survLIME, an extension of Local Interpretable Model-agnostic Explanations (LIME) to survival data. Performance was evaluated using synthetic data and applied to clinical trials examining the effectiveness of panitumumab to treat metastatic colorectal cancer. An area under the receiver operating characteristic curve (AUC) of 0.77 for identifying fixed responders was achieved in a 1000 patient simulation. When considering dynamic responders, partly conditional modelling increased the AUC from 0.597 to 0.685. Applying the approach to colorectal cancer trials found genetic mutations, sites of metastasis, and ethnicity as important factors for response to treatment. Our approach can accommodate a dynamic response to treatment while potentially providing better performance than existing methods in instances of a fixed response to treatment. When applied to clinical data we attain results consistent with the literature.

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