Bridging prediction and causality: evaluating algorithms that predict treatment benefit

A treatment benefit predictor is a function that maps patient characteristics to a putative treatment benefit for that patient. Such predictors support the optimization of individualized treatment decisions, a central idea of precision medicine. However, evaluating the predictive performance of a treatment benefit predictor is challenging, as we often cannot observe each individual's treatment benefit. This work theoretically underpins common predictive metrics and demonstrates conceptual and practical evaluation of prespecified treatment benefit predictors in the target population. At a conceptual level, we define the estimands of a set of predictive performance metrics. A particular measure of discrimination is used as an illustrative example to reveal methodological concerns on multiple fronts. We describe how to evaluate a treatment benefit predictor using observational data from the target population and explore how predictive performance metrics may change when confounding is not fully controlled. In practice, we propose and implement estimation methods for evaluating the predictive performance of treatment benefit predictors, assessing their reliability through simulation studies. We illustrate their practical use in real-world observation data, including cohort construction and modeling strategies. Overall, this work helps bridge the gap between predictive modeling and causal inference, providing a framework for evaluating treatment benefit predictors using predictive performance metrics.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.