Talk presented at SDSS 2022 as part of the High-Dimensional Statistics session.
An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. However, the identification of such biomarkers is often the byproduct of the related, but fundamentally different, task of treatment rule estimation. Applying treatment rule estimation methods to identify predictive biomarkers often results in high false discovery rates. The higher than expected number of false positives may translate to waste of resources when conducting follow up experiments of drug target identification and diagnostics assay development. Patient outcomes are in turn negatively affected. We propose a variable importance measure for directly assessing the importance of potentially predictive biomarkers, and develop a flexible semiparametric estimation procedure, uniCATE, for this parameter. We prove that our estimator is double-robust and asymptotically linear under loose conditions on the data-generating process, permitting valid inference about the metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from non-predictive biomarkers than procedures whose primary purpose is treatment rules estimation, and that these biomarkers delineate more clinically relevant patient subpopulations. A open-source software for the R programming language of the same name, uniCATE, will be made available for general use.