Gong Tang

  • Professor
  • Senior Statistician and Associate Director, The NRG Oncology Statistics and Data Management Center Pittsburgh

My primary research interests include analysis of missing data, design, implementation and analysis of clinical trials, and statistical machine learning. Other research interests are longitudinal data analysis, semi-parametric statistics, diagnostic tests without gold standard, modeling with stochastic processes, and analysis of gene expression data. The fields of application include breast cancers, environmental health in petroleum industry workers, reproductive health of women, pancreatitis, critical care medicine and inflammatory bowel disease.


2001 | University of Michigan, Ann Arbor, MI | PhD of Biostatistics

1996 | Johns Hopkins University, Baltimore, MD | MA of Mathematics

1994 | Beijing University, Beijing, China | MS of Mathematics

1991 | Beijing University, Beijing, China | BS of Mathematics

2013 | Chapter Service Award | The Council of Chapters, American Statistical Association

2010 | Best of ASCO Abstract | The American Society of Clinical Oncology Annual Meeting 2010

2001 | Student Paper Award | The Eastern Northern Region of the International Biometric Society


Fall, 2021 | Biostat 2065: Analysis of Incomplete Data

Spring, 2022 | Biostat 2061: Likelihood theory and applications

Selected Publications

1. Tang G, Little RJ, Raghunathan TE. Analysis of Multivariate Missing Data with Nonignorable Nonresponse. Biometrika. 2003; 4 (90):747-764.

2. Tang G, Little RJ, Raghunathan TE. Analysis of
Multivariate Monotone Missing Data by a Pseudolikelihood Method. In: Lin D,
Heagerty PJ, editors. Proceedings of the Second Seattle Symposium in
Biostatistics: Analysis of Correlative Data. Lecture Notes in Statistics. New
York: Springer-Verlag, 2004.

3. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England journal of medicine. 2004 Dec 30; 351 (27):2817-26. PMID: 15591335.

4. Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE Jr, Wickerham DL, Wolmark N. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2006 Aug 10; 24 (23):3726-34. PMID: 16720680.

5. Tang G, Shak S, Paik S, Anderson SJ, Costantino JP, Geyer CE Jr, Mamounas EP, Wickerham DL, Wolmark N. Comparison of the prognostic and predictive utilities of the 21-gene Recurrence Score assay and Adjuvant! for women with node-negative, ER-positive breast cancer: results from NSABP B-14 and NSABP B-20. Breast cancer research and treatment. 2011 May; 127 (1):133-42. PMID: 21221771.

6. Haggerty CL, Seifert ME, Tang G, Olsen J, Bass DC, Karumanchi SA, Ness RB. Second trimester anti-angiogenic proteins and preeclampsia. Pregnancy Hypertension: An International Journal of Women’s Cardiovascular Health. 2012; 2 (2):158-163. PMCID: PMC3375839. PMID: 22712058.

7. Bear HD, Tang G, Rastogi P, Geyer CE Jr, Robidoux A, Atkins JN, Baez-Diaz L, Brufsky AM, Mehta RS, Fehrenbacher L, Young JA, Senecal FM, Gaur R, Margolese RG, Adams PT, Gross HM, Costantino JP, Swain SM, Mamounas EP, Wolmark N. Bevacizumab added to neoadjuvant chemotherapy for breast cancer. The New England journal of medicine. 2012 Jan 26; 366 (4):310-20. PMCID: PMC3401076. PMID: 22276821.

8. Schnatter AR, Glass DC, Tang G, Irons RD, Rushton L. Myelodysplastic syndrome and benzene exposure among petroleum workers: an international pooled analysis. Journal of the National Cancer Institute. 2012 Nov 21; 104 (22):1724-37. PMCID: PMC3502195. PMID: 23111193.

9. Zhu F, Tang G. An Index of Local Sensitivity to Nonignorability for a Pseudolikelihood Method. Communications in Statistics - Theory and Methods. 2013 Feb; 42 (6):954-973.

10. Crager M, Tang G. Patient-specific meta-analysis for risk assessment using multivariate proportional hazards regression. Journal of Applied Statistics. 2014 Dec; 41 (12):2676-2695.

11. Abberbock, J., Anderson, S., Rastogi, P., & Tang, G. (2019). Assessment of effect size and power for survival analysis through a binary surrogate endpoint in clinical trials. Statistics in Medicine, 38(3), 301-314. doi:10.1002/sim.7981

12. Swain SM, Tang G, Brauer HA, Goerlitz DS, Lucas PC, Robidoux A, Harris BT, Bandos H, Ren Y, Geyer CE, Rastogi P, Mamounas EP, Wolmark N. NSABP B-41, a Randomized Neoadjuvant Trial: Genes and Signatures Associated with Pathologic Complete Response. Clin Cancer Res. 2020 May 5;. doi: 10.1158/1078-0432.CCR-20-0152. [Epub ahead of print] PubMed PMID: 32371537.

13. Sparano JA, Crager MR, Tang G, Gray RJ, Stemmer SM, Shak S. Development and Validation of a Tool Integrating the 21-Gene Recurrence Score and Clinical-Pathological Features to Individualize Prognosis and Prediction of Chemotherapy Benefit in Early Breast Cancer. J Clin Oncol. 2021 Feb 20;39(6):557-564.

14. Tan X, Abberbock J, Rastogi P, and Tang G. Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response. Accepted by Proceedings of Machine Learning Research. Presented at the Causal Learning and Reasoning 2022 conference.

15. Wang XS, Lee S, Zhang H, Tang G, Wang Y. An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. Nat Commun. 2022 May 26;13(1):2936. doi: 10.1038/s41467-022-30449-7. PubMed PMID: 35618721.