@article{yang_zhang_2022, title={Multiply robust matching estimators of average and quantile treatment effects}, volume={4}, ISSN={["1467-9469"]}, DOI={10.1111/sjos.12585}, abstractNote={Abstract}, journal={SCANDINAVIAN JOURNAL OF STATISTICS}, author={Yang, Shu and Zhang, Yunshu}, year={2022}, month={Apr} }
 @article{zhang_yang_ye_faries_lipkovich_kadziola_2021, title={Practical recommendations on double score matching for estimating causal effects}, volume={12}, ISSN={["1097-0258"]}, DOI={10.1002/sim.9289}, abstractNote={Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at 
https://github.com/Yunshu7/dsmatch.}, journal={STATISTICS IN MEDICINE}, author={Zhang, Yunshu and Yang, Shu and Ye, Wenyu and Faries, Douglas E. and Lipkovich, Ilya and Kadziola, Zbigniew}, year={2021}, month={Dec} }
 @article{rashid_luckett_chen_lawson_wang_zhang_laber_liu_yeh_zeng_et al._2020, title={High-Dimensional Precision Medicine From Patient-Derived Xenografts}, volume={116}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2020.1828091}, abstractNote={Abstract The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.}, number={535}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Rashid, Naim U. and Luckett, Daniel J. and Chen, Jingxiang and Lawson, Michael T. and Wang, Longshaokan and Zhang, Yunshu and Laber, Eric B. and Liu, Yufeng and Yeh, Jen Jen and Zeng, Donglin and et al.}, year={2020}, month={Nov}, pages={1140–1154} }
 @article{lu_zhang_ding_2020, title={Sharp bounds on the relative treatment effect for ordinal outcomes}, volume={76}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13148}, abstractNote={Abstract}, number={2}, journal={BIOMETRICS}, author={Lu, Jiannan and Zhang, Yunshu and Ding, Peng}, year={2020}, month={Jun}, pages={664–669} }