A framework for estimating and testing qualitative interactions with applications to predictive biomarkers. (24th August 2017)

• Record Type: Journal Article
• Authors: Roth, Jeremy; Simon, Noah
• Is Part Of: Biostatistics. Volume 19:Number 3(2018)
• Page Start: 263
• Page End: 280
• Publication Date: 2017-08-24
• Subjects: Additive models -- Biomarker -- Fused lasso -- Treatment rule; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxx038
• Languages: English
• ISSNs: 1465-4644

Abstract

SUMMARY: 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 ).