About Yu (Joyce) Jiang

Dr. Yu (Joyce) Jiang is an Assistant Professor in the Division of Epidemiology, Biostatistics, and Environmental Health. Her general research interests include Bayesian data analysis, clinical trial studies, cancer epidemiology and cancer genomics. As a biostatistician, she has broad interests in collaborating with researchers in biological science, medicine, public health and all other related fields.

Education

• PhD, Biostatistics, University of Kansas Medical Center, Kansas City, KS
• PhD, Human Nutrition, Kansas State University, Manhattan, KS.

Research Interests

• Bayesian methods
• Clinical trials
• Statistical genetics and bioinformatics
• Cancer Epidemiology
• Latent variable modeling

Selected Publications

1. Nikhil A. Ahuja, Satish K. Kedia, Yu Jiang, Kenneth D. Ward, Latrice C. Pichon, Patrick J. Dillon, Xinhua Yu & Lu Xie (2022) Factors Associated with E-Cigarette Quit Intention Among Adolescents in the United States, Substance Use & Misuse, 57:14, 2074-2084, DOI: 10.1080/10826084.2022.2130000.
2. Chen, P., Reed, G., Jiang, J. et al. Pharmacokinetic Evaluation of Intravenous Vitamin C: A Classic Pharmacokinetic Study. Clin Pharmacokinet 61, 1237–1249 (2022). https://doi.org/10.1007/s40262-022-01142-1.
3. Lv S, Jiang Y, Li Y, Huang R, Peng L, Ma Z, Lu N, Lin X, Yan J. Comparative and evolutionary analysis of RIP kinases in immune responses. Front Genet. 2022 Oct 3;13:796291. doi: 10.3389/fgene.2022.796291. PMID: 36263437; PMCID: PMC9573974.
4. Da BL, Satiya J, Heda RP, Jiang Y, Lau LF, Fahmy A, Winnick A, Roth N, Grodstein E, Thuluvath PJ, Singal AK, Schiano TD, Teperman LW, Satapathy SK. Outcomes after Liver Transplantation with Steatotic Grafts: Redefining Acceptable Cutoffs for Steatotic Grafts. Euroasian J Hepatogastroenterol. 2022 Jul;12(Suppl 1):S5-S14. doi: 10.5005/jp-journals-10018-1361. PMID: 36466105; PMCID: PMC9681573.
5. Mary E. Rinella, Sanjaya K. Satapathy, Danielle Brandman, Coleman Smith, Sal Elwir, Jonathan Xia, Meg Gibson, Carlos Figueredo, Mounika Angirekula, Jason M. Vanatta, Raiya Sarwar, Yu Jiang, Dyanna Gregory, Tandy Agostini, JimIn Ko, Pradeep Podila, Grace Gallo, Kymberly D. Watt, Mohammad S. Siddiqui. Factors Impacting Survival in Those Transplanted for NASH Cirrhosis: Data From the NailNASH Consortium. Clinical Gastroenterology and Hepatology. Volume 21, Issue 2, 2023; Pages 445-455.e2. ISSN 1542-3565. https://doi.org/10.1016/j.cgh.2022.02.028.
6. Giammarino, A.M., Qiu, H., Bulsara, K., Khan, S., Jiang, Y., Da, B.L., Bernstein, D.E. and Satapathy, S.K. (2022), Community Socioeconomic Deprivation Predicts Nonalcoholic Steatohepatitis. Hepatol Commun, 6: 550-560. https://doi.org/10.1002/hep4.1831
7. Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y., Wu, C. Robust Bayesian variable selection for gene–environment interactions. Biometrics. 2022; 00 1– 11. https://doi.org/10.1111/biom.13670.
8. Jiang Y, Guarino P, Ma S, Simon S, Mayo MS, Raghavan R, Gajewski BJ. Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application.Trials. 2016; 17(1):336.
9. Jiang Y, Shi X, Zhao Q, Krauthammer M, Rothberg BE, Ma S. Integrated analysis of multidimensional omics data on cutaneous melanoma prognosis. Genomics. 2016; 107 (6):223-30. (The first two authors contributed equally)
10. Jiang Y, Simon S, Mayo MS, Gajewski BJ, Modeling and Validating Bayesian Accrual Models on Clinical Data and Simulations Using Adaptive priors. Stat Med. 2015;34(4):613-29.
11. Jiang Y, Boyle DK, Bott MJ, Wick JA, Yu Q, Gajewski BJ. Expediting clinical and translational research via Bayesian instrument development. Applied Psychological Measurement. 2014, 38(4):296-310.
12. Gajewski BJ, Jiang Y, Yeh H, Engelman K, Teel C, Choi W, Greiner K, Daley C, Teaching confirmatory factor analysis to non-statisticians: A case study for estimating reliability of psychometric instruments. Case Studies in Business, Industry, and Government Statistics. 2014, 5(2): 88-101.
13. Yeh H-W, Jiang Y, Garrard L, Lei Y, Gajewski BJ, A Bayesian model for censored positive count data in evaluating breast cancer progression. Model Assisted Statistics and Applications. 2013, 8(2):143-150.

Software Packages

Subject recruitment for clinical research is crucial and challenging. The R package provides a tool for researchers to predict accrual in a clinical study.  The package has a graphic user friendly interface. It can be easily used by statisticians and clinical researchers.  Please click here to access the accrual R package.