@article{faries_gao_zhang_hazlett_stamey_yang_ding_shan_sheffield_dreyer_2024, title={Real Effect or Bias? Good Practices for Evaluating the Robustness of Evidence From Comparative Observational Studies Through Quantitative Sensitivity Analysis for Unmeasured Confounding}, volume={12}, ISSN={["1539-1612"]}, DOI={10.1002/pst.2457}, abstractNote={ABSTRACT The assumption of “no unmeasured confounders” is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real‐world evidence remains under‐utilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements for application of each method. With the advent of methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder—along with publicly available code for implementation—roadblocks toward broader use of sensitivity analyses are decreasing. To spur greater application, here we offer a good practice guidance to address the potential for unmeasured confounding at both the design and analysis stages, including framing questions and an analytic toolbox for researchers. The questions at the design stage guide the researcher through steps evaluating the potential robustness of the design while encouraging gathering of additional data to reduce uncertainty due to potential confounding. At the analysis stage, the questions guide quantifying the robustness of the observed result and providing researchers with a clearer indication of the strength of their conclusions. We demonstrate the application of this guidance using simulated data based on an observational fibromyalgia study, applying multiple methods from our analytic toolbox for illustration purposes.}, journal={PHARMACEUTICAL STATISTICS}, author={Faries, Douglas and Gao, Chenyin and Zhang, Xiang and Hazlett, Chad and Stamey, James and Yang, Shu and Ding, Peng and Shan, Mingyang and Sheffield, Kristin and Dreyer, Nancy}, year={2024}, month={Dec} }
 @article{zhang_li_zhou_zhou_shen_2019, title={TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS}, volume={29}, ISSN={["1996-8507"]}, DOI={10.5705/ss.202017.0153}, abstractNote={Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative.}, number={4}, journal={STATISTICA SINICA}, author={Zhang, Xiang and Li, Lexin and Zhou, Hua and Zhou, Yeqing and Shen, Dinggang}, year={2019}, month={Oct}, pages={1977–2005} }
 @article{zhang_wu_wang_li_2016, title={A consistent information criterion for support vector machines in diverging model spaces}, volume={17}, journal={Journal of Machine Learning Research}, author={Zhang, X. and Wu, Y. C. and Wang, L. and Li, R. Z.}, year={2016} }
 @article{novick_zhang_yang_2016, title={A new PK equivalence test for a bridging study}, volume={26}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2016.1148712}, abstractNote={ABSTRACT In a bridging study, the plasma drug concentration–time curve is generally used to assess bioequivalence between the two formulations. Selected pharmacokinetic (PK) parameters including the area under the concentration–time curve, the maximum plasma concentration or peak exposure (Cmax), and drug half-life (T1/2) are compared to ensure comparable bioavailability of the two formulations. Comparability in these PK parameters, however, does not necessarily imply equivalence of the entire concentration–time profile. In this article, we propose an alternative metric of equivalence based on the maximum difference between PK profiles of the two formulations. A test procedure based on Bayesian analysis and accounting for uncertainties in model parameters is developed. Through both theoretical derivation and empirical simulation, it is shown that the new method provides better control over consumer’s risk.}, number={5}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Novick, Steven J. and Zhang, Xiang and Yang, Harry}, year={2016}, pages={992–1002} }
 @inproceedings{zhang_brown_shankar_2016, title={Data-driven personas: Constructing archetypal users with clickstreams and user telemetry}, booktitle={34th Annual CHI Conference on Human Factors in Computing Systems, CHI 2016}, author={Zhang, X. and Brown, H. F. and Shankar, A.}, year={2016}, pages={5350–5359} }
 @article{zhang_wilson_2017, title={System Reliability and Component Importance Under Dependence: A Copula Approach}, volume={59}, ISSN={["1537-2723"]}, DOI={10.1080/00401706.2016.1142907}, abstractNote={ABSTRACT System reliability and component importance are of great interest in reliability modeling, especially when the components within the system are dependent. We characterize the influence of dependence structures on system reliability and component importance in coherent systems with discrete marginal distributions. The effects of dependence are captured through copula theory. We extend our framework to coherent multi-state system. Applications of the derived results are demonstrated using a Gaussian copula, which yields simple interpretations. Simulations and two examples are presented to demonstrate the importance of modeling dependence when estimating system reliability and ranking of component importance. Proofs, algorithms, code, and data are provided in supplementary materials available online.}, number={2}, journal={TECHNOMETRICS}, author={Zhang, Xiang and Wilson, Alyson}, year={2017}, pages={215–224} }
 @inproceedings{zhang_brown_shankar_2015, title={Data-driven personas: Constructing archetypal users with clickstreams and user telemetry}, booktitle={34th Annual CHI Conference on Human Factors in Computing Systems, CHI 2016}, author={Zhang, X. and Brown, H. F. and Shankar, A.}, year={2015}, pages={5350–5359} }
 @article{zhang_wu_wang_li_2016, title={Variable selection for support vector machines in moderately high dimensions}, volume={78}, ISSN={["1467-9868"]}, DOI={10.1111/rssb.12100}, abstractNote={Summary
               The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when the number of predictors diverges to ∞. We establish a unified theory for a general class of non-convex penalized SVMs. We first prove that, in ultrahigh dimensions, there is one local minimizer to the objective function of non-convex penalized SVMs having the desired oracle property. We further address the problem of non-unique local minimizers by showing that the local linear approximation algorithm is guaranteed to converge to the oracle estimator even in the ultrahigh dimensional setting if an appropriate initial estimator is available. This condition on the initial estimator is verified to be automatically valid as long as the dimensions are moderately high. Numerical examples provide supportive evidence.}, number={1}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY}, author={Zhang, Xiang and Wu, Yichao and Wang, Lan and Li, Runze}, year={2016}, month={Jan}, pages={53–76} }