Current literature focus on deriving individualized treatment rules (ITRs) from a single source population. We consider the setting when the source population may differ from the target population of interest. We assume subject covariates are available from both populations, but treatment and outcome data are only available from the source population. Although adjusting for differences between source and target populations can potentially lead to an improved ITR for the target population, it can substantially increase the variability in ITR estimation. To address this dilemma, we develop a weighting framework that aims to tailor an ITR for a given target population and protect against high variability due to superfluous covariate shift adjustments. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space. We show that the proposed method encompasses the so-called importance weights and overlap weights as two extreme cases, allowing for searching a better bias-variance trade-off. Numerical examples demonstrate that using our weighting methods greatly improves ITR estimation for the target population compared with other weighting methods.
I am an Assistant Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. I got my Ph.D. from the University of North Carolina at Chapel Hill in 2014 under the direction of Dr. Michael Kosorok. Before joining UW, I was an Assistant Professor of Biostatistics at Vanderbilt University.
Develop statistical learning methods for clinical and biomedical research. In particular, I am interested in analyzing heterogeneous, high-dimensional-omics data (genome, microbiome) and electronic health record data to advance precision medicine. My current research is supported by PCORI and NSF grants.