@article{zhu_2020, title={Correction}, ISBN={1097-0258}, DOI={10.1002/sim.8527}, abstractNote={Clinical Article Br Dent J 2018; 225: 293–298 Space maintainers in the primary and mixed dentition – a clinical guide In the author listing for this paper, one of the author names (A. Katsimpali) was spelt incorrectly. The correct author listing reads as follows: E. Watt, A. Ahmad, R. Adamji, A. Katsimpali, P.}, journal={STATISTICS IN MEDICINE}, author={Zhu, Rui}, year={2020} }
 @article{zhu_ghosal_2019, title={Bayesian Semiparametric ROC surface estimation under verification bias}, volume={133}, ISSN={0167-9473}, url={http://dx.doi.org/10.1016/J.CSDA.2018.09.003}, DOI={10.1016/j.csda.2018.09.003}, abstractNote={The Receiver Operating Characteristic (ROC) surface is a generalization of the ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. Verification bias occurs when not all subjects have their labels observed. This is a common problem in disease diagnosis since the gold standard test to get labels, i.e., the true disease status, can be invasive and expensive. The same situation happens in the evaluation of semi-supervised learning, where the unlabeled data are incorporated. A Bayesian approach for estimating the ROC surface is proposed based on continuous data under a semi-parametric trinormality assumption. The proposed method is then extended to situations in the presence of verification bias. The posterior distribution is computed under the trinormality assumption using a rank-based likelihood. The consistency of the posterior under a mild condition is also established. The proposed method is compared with existing methods for estimating an ROC surface. Simulation results show that it performs well in terms of accuracy. The method is applied to evaluate the performance of CA125 and HE4 in the diagnosis of epithelial ovarian cancer (EOC) as a demonstration.}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={Zhu, Rui and Ghosal, Subhashis}, year={2019}, month={May}, pages={40–52} }
 @article{zhu_ghosal_2019, title={Bayesian nonparametric estimation of ROC surface under verification bias}, volume={38}, ISSN={["1097-0258"]}, DOI={10.1002/sim.8181}, abstractNote={The receiver operating characteristic (ROC) surface, as a generalization of the ROC curve, has been widely used to assess the accuracy of a diagnostic test for three categories. A common problem is verification bias, referring to the situation where not all subjects have their true classes verified. In this paper, we consider the problem of estimating the ROC surface under verification bias. We adopt a Bayesian nonparametric approach by directly modeling the underlying distributions of the three categories by Dirichlet process mixture priors. We propose a robust computing algorithm by only imposing a missing at random assumption for the verification process but no assumption on the distributions. The method can also accommodate covariates information in estimating the ROC surface, which can lead to a more comprehensive understanding of the diagnostic accuracy. It can be adapted and hugely simplified in the case where there is no verification bias, and very fast computation is possible through the Bayesian bootstrap process. The proposed method is compared with other commonly used methods by extensive simulations. We find that the proposed method generally outperforms other approaches. Applying the method to two real datasets, the key findings are as follows: (1) human epididymis protein 4 has a slightly better diagnosis ability compared to CA125 in discriminating healthy, early stage, and late stage patients of epithelial ovarian cancer. (2) Serum albumin has a prognostic ability in distinguishing different stages of hepatocellular carcinoma.}, number={18}, journal={STATISTICS IN MEDICINE}, author={Zhu, Rui and Ghosal, Subhashis}, year={2019}, month={Aug}, pages={3361–3377} }