A framework for estimating and testing qualitative interactions with applications to predictive biomarkers.

An effective treatment may only benefit a subset of patients enrolled in a clinical trial. We translate the search for patient characteristics that predict treatment benefit to a search for qualitative interactions, which occur when the estimated response-curve under treatment crosses the estimated response-curve under control. We propose a regression-based framework that tests for qualitative interactions without assuming linearity or requiring pre-specified risk strata; this flexibility is useful in settings where there is limited a priori scientific knowledge about the relationship between features and the response. Simulations suggest that our method controls Type I error while offering an improvement in power over a procedure based on linear regression or a procedure that pre-specifies evenly spaced risk strata. We apply our method to a publicly available dataset to search for a subset of HER2+ breast cancer patients who benefit from adjuvant chemotherapy. We implement our method in Python and share the code/data used to produce our results on GitHub (https://github.com/jhroth/data-example).