Office Hours for Spring 2014

Qing Pan

Qing Pan

Title:
Associate Professor
Faculty: Full-Time
Office:
752
Address: Rome Hall
801 22nd St NW
Washington, DC,
20052
Phone: 202-994-6359
Email:
[email protected]
Website:

Areas of Expertise

Survival analysis, clinical trials, electronic health records, "omics" data, network analysis, equal employment opportunity.

Pan worked/works on the Prospective Payment System project for end stage renal disease patients, Scientific Registry of Transplantation Recipients, Diabetes Prevention Program Outcome Study and Antibacterial Resistance Leadership Group. She receives the mentored research career award in clinical translational research from NIH during 2012-2014, studying genetic disparity associated with composite microvascular complications in Type 1 Diabetes patients.

Locating in DC, her other reseach area  is statistical issues in legal applications.

Education

Ph.D. 2007, University of Michigan

Publications

PEER-REVIEWED PUBLICATIONS: Statistical

  1. Pan, Q.,  Gastwirth, J.L. and Miao W.W. One-sided T2 test for assessing the need for an Affirmative Action plan: A reanalysis of the Shea v. Kerry (Statistics & Public Policy) PDF
  2. Jiang, Y.*, Pan, Q.*, Liu, Y. and Evans, S. A Statistical Review: Why Average Weighted Accuracy, not Accuracy or AUC? (co-first author, Journal of Biostatistics & Epidemiology)
  3. Wang H, Hueman MT, Pan Q, Henson DE, Schwartz AM, Sheng L, Chen DC. Creating Cancer Staging Systems by the Mann-Whitney Parameter. The 3rd IEEE/ACM conference on connected health: Applications, Systems and Engineering Technologies. 2018 Smart Health Journal: 18474377. PDF
  4. Cheung, L.C., Pan, Q., Hyun, N. and Hormudz, K. Prioritized concordance index for hierarchical survival outcomes. Statistics in Medicine (2019) 38(15): 2868-2882. PDF
  5. Zhao, Y., Pan, Q. and Du, C. Logistic regression augmented community detection. Biometrics. 2019;75(1):222-234. PDF
  6. Yang, A., Miller, D. and Pan, Q.* Constrained maximum entropy models to select genetype interactions associated with censored failure times.Journal of Bioinformatics & Computational Biology (2018) 16(6).  PDF
  7. Cheung, L., Pan, Q., Hyun, N., Schiffman, M., Fetterman, B., Castle, P.E. and Hormudz, K. Mixture models for undiagnosed prevalent disease and interval-censored incident disease: Application to a cohort assembled from electronic health records. Statistics in Medicine (2017) 36(22): 3583-3595. PDF
  8. Hyun, N., Cheung, L., Pan, Q., Schiffman, M. and Hormuzd, K.  Flexible risk prediction models for left or interval-censored data from electronic health records, Annals of Applied Statistics (2017) 11(2):1063—1084. PDF
  9. Gastwirth JL, Miao W, Pan Q. Statistical issues arising in Kerner v. Denver, a class action disparate impact case. Law, Probability & Risk, (2017) 16 (1): 35-53. PDF
  10. Pan Y, Yan C, Hu Y, Wan Q, Pan Q, Torcivia  J, Mazumder R. Distribution bias analysis of germline and somatic single-nucleotide variations that impact protein functional site and neighboring amino acids. Scientific Reports – Nature, 2017, 7:42169. PDF
  11. Pan, Q. and Zhao, Y. Integrative weighted group lasso and generalized local quadratic approximation. Computational Statistics & Data Analysis, 2016, 104: 66-78. PDF
  12. Gastwirth, J.L., Xu, W. and Pan, Q. Statistical methods for evaluating minority under-representation on juries and venires: Analysis of the conflicting inferences drawn from the same data in People v. Bryant and Ambrose v. Booker. Law, Probability & Risk 2015, 14(4): 279-304. PDF
  13. Gastwirth, J.L. Xu, W. and Pan. Q. Did the Michigan Supreme Court Appreciate the Implications of Adopting the “Disparity of the Risk” Measure of Minority Representation in Jury Pools in People v. Bryant? Statistics and Public Policy 2014, 1, 129-132. PDF
  14. Xu, W., Pan, Q. and Gastwirth, J.L. Proportional hazards models with frailty for negatively correlated processes. Computational Statistics & Data Analysis 2014, 70: 295-307. PDF
  15. Xu, W., Pan, Q. and Gastwirth, J.L. Adaptive procedures for nested processes: Application to equal employment. Statistics & Its Inference 2014, 7(2): 153-165. PDF
  16. Pan, Q. and Schaubel, D.EProportional hazards regression in the presence of missing study eligibility information. Lifetime Data Analysis, 2014, 20(3): 424-443. PDF
  17. Pan, Q. Multiple testing methods in clinical trials and genomic studies. Frontiers in Public Health 2013, 1(63): 1-8.
  18. Pan, Q. and Gastwirth, J.L. Estimating restricted mean job tenures in semi-competing risk data compensating victims of discrimination. Annals of Applied Statistics, 2013, 7(3): 1474-1496.
  19. Pan, Q. Case Comment: Adams v. Perrigo and the appropriate criterion for bioequivalence in patent infringement cases. Law, Probability & Risk 2013, 12(2): 147-153  
  20. Pan, Q. and Gastwirth, J.L. The appropriateness of survival analysis for determining lost pay in discrimination cases: application of the ‘Lost Chance’ doctrine to Alexander v. Milwaukee.  Law, Probability & Risk  2013, 12(1): 13-35
  21. Pan, Q. and Grace, Y.YAn estimation method of marginal treatment effects on correlated longitudinal and survival outcomes. Statistics and Its Inference 2011, 4(4): 499-509.
  22. Gastwirth, J.L. and Pan, Q. Statistical measures and methods for assessing the representativeness of juries: a reanalysis of the data in Berghuis v. Smith. Law, Probability & Risk 2011, 10(1): 17-57
  23. Pan, Q. and Schaubel, D.EEvaluating bias correction in weighted proportional hazards regression.  Lifetime Data Analysis 2009, 15(1): 120-146.
  24. Pan, Q. and Schaubel, D.EFlexible estimation of differences in treatment-specific recurrent event means in the presence of a terminating event.  Biometrics 2009, 65(3): 753-761.
  25. Pan, Q. and Gastwirth, J.L. Issues in the use of survival analysis to estimate damages in equal employment cases. Law, Probability & Risk 2009, 8(1): 1-24
  26. Gastwirth, J.L. and Pan, Q. Diaz v. Eagle Produce Ltd. Partnership: the potential for and limitations of formal statistical analysis to assist courts when drawing inferences from a relatively small data set. Jurimetrics 2009, 49(4): 439-466.
  27. Sinclair, M.D. and Pan, Q. Using the Peters-Belson Method in EEO personnel evaluations (with Comments). Law, Probability & Risk 2009, 8(2): 95-117
  28. Pan, Q. and Schaubel, D.EWeighted Cox regression with biased samples and empirically estimated selection probabilities. Canadian Journal of Statistics 2008, 36(1): 111-127.

PEER-REVIEWED PUBLICATIONS: Clinical

  1. Tan X., Li Z., Ren S., Rezaei K., Pan Q.,  Goldstein A.,  Macri C., Brem R.F.  and Fu S.W. Dynamically decreased miR-671-5p expression is associated with oncogenic transformation and radiochemoresistance in breast cancer. Breast Cancer Research 2019, 21(1):89. PDF
  2. Perreault L, Pan Q, Connor EB, Bray G, Dabelea D, Jack SD, Goldberg R, Kahn S, Kalyani R, Knowler W, Mathiodakis N, Schroeder E, White N. Cardiovascular and Metabolic Risk - Regression from Prediabetes to Normal Glucose Regulation and Prevalence of Microvascular Disease in the Diabetes Prevention Program Outcomes Study. Diabetes Care, 2019, doi: 10.2337/dc19-0244. PDF
  3. Chang TC, Goud SM,Torcivia-Rodriguez J, Hu Y, Pan Q, Kahsay R, Blomberg J, Mazumder R. Investigation of Somatic Single Nucleotide Variations in Human Endogenous Retrovirus Elements and Their Potential Association with Cancer PLoS One. 2019 Apr 1;14(4):e0213770. PDF
  4. Herman WH, Pan Q, Edelstein SL, Mather KJ, Perreault L, Barrett-Conner E, Dabelea DM, Horton E, Kahn SE, Knowler WC, Lorenzo C, Pi-Sunyer X, Venditti E, Ye W. Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People with Impaired Glucose Regulation. Diabetes Care. 2017, 40(12):1668-1677. PDF
  5. Perreault, L., Pan, Q., Mather, K., Watson, K., Kahn, S., Knowler, W., Barrett-Connor, E., Dabelea, D., Vanita, A. and Hamman, R. Exploring Residual Risk for Diabetes and Microvascular Disease in the Diabetes Prevention Program Outcomes Study. Diabetic Medicine 2017, 34(12):1747-1755. PDF
  6. Mather KJ, Pan Q, Knowler WC, Funahashi T, Bray GA, Arakaki R, Falkner B, Sharma K, and Goldstein BJ. Treatment-induced changes in plasma adiponectin do not affect urinary albumin excretion in the Diabetes Prevention Program cohort. Plos One 2015, 10(8): e0136853. PDF
  7. Papandonatos G.D., Pan Q., Pajewski N., Delahanty L.M., Peter I., Erar B., Ahmad S., Harden M., Chen L., Fontanillas P., Wagenknecht L., Kahn S., Wing R., Jablonski K., Huggins G., Knowler W., Florez J., McCaffery J., Franks P. Genetic predisposition to weight loss & regain with lifestyle intervention: Analyses from the Diabetes Prevention Program & the Look AHEAD randomized controlled trials. Diabetes Care 2015, 64:4312-4321. PDF
  8. Delahanty, L.M., Pan, Q., Jablonski, K.A., Aroda, V.R., Watson, K., Bray, G.A., Kahn, S.E., Florez, J.C., Perreault, L. and Franks, P.W. Effects of weight loss, weight cycling and weight loss maintenance on diabetes incidence and cardiometabolic traits in the diabetes prevention program. Diabetes Care 2014 Oct, 37(10): 2738-45. PDF
  9. Marrero, D., Pan, Q., Barrett-Connor, E., de Groot, M., Zhang, P., Percy, C., Florez, H., Ackermann, R., Montez, M. and Rubin, R.R., Impact of diagnosis of diabetes on health-related quality of life among high risk individuals: the DPP outcome study. Quality of Life Research 2014, 23(1), 75-88.PDF
  10. Pan, Q., Delahanty, L.M., Jablonski, K.A., Knowler, W.C., Kahn, S.E., Florez, J.C. and Franks, P.W.  Variation at the melanocortin 4 receptor gene and response to weight-loss interventions in the diabetes prevention program. Obesity 2013 Sep, 21(9): E520-6.
  11. Pollin, T. I., Isakova, T.,  Jablonski, K.A., de Bakker, P.I.W., Taylor, A.,  McAteer, J.,  Pan, Q., Horton, E.S., Delahanty, L.M., Altshuler, D., Shuldiner, A.R., Goldberg, R.B., Florez, J.C. and Franks, P.W. Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: the diabetes prevention program. PloS Genetics 2012, 8(8): e1002895
  12. Florez, H., Pan, Q., Rubin, R., Ackermann, R.T., Marrero, D.G., Barrett-Connor, E., Delahanty, L., Kriska, A., Saudek, C.D., Goldberg, R.B. and Rubin, R.R. Impact of lifestyle intervention and metformin on health-related quality of life: the diabetes prevention program randomized trial. Journal of General Internal Medicine 2012, 27(12): 1594-1601
  13. Perreault, L., Pan, Q., Mather, K.J., Watson, K.E., Saudek, C., Hamman, R.F. and Kahn, S.E. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet 2012, 379(9833): 2243-2251
  14. Delahanty, L.M., Pan, Q., Jablonski, K.A., Watson, K.E., McCaffery, J.M., Shuldiner, A., Kahn, S.E., Knowler, W.C., Florez, J.C. and Franks, P.W. Genetic predictors of weight loss and weight regain after intensive lifestyle modification, metformin treatment or standard care in the diabetes prevention program. Diabetes Care 2012, 35(2): 363-366
  15. Damle, S., Teal, C.B., Lenert, J.J., Marshall, E.C., Pan, Q. and Mcswain, A.P. Mastectomy and contralateral prophylactic mastectomy rates: an institutional review.  Annals of Surgical Oncology 2011, 18(5): 1356-1363
  16. Hirth, R.A., Pan, Q.,  Schaubel, D.E. and Merion, R.M. Efficient utilization of the Expanded Criteria Donor (ECD) deceased donor kidney pool: an analysis of the effect of labeling. American Journal of Transplantation 2010, 10(2): 304-309.
  17. Hirth, R.A., Turenne, M.N., Wheeler, J.R.C., Pan, Q., Ma, Y. and Messana, J.M. Provider monitoring and pay-for-performance when multiple providers affect outcomes: an application to renal dialysis. Health Service Research 2009, 44(5): 1585-1602
  18. Turenne, M.N., Hirth, R.A., Pan, Q., Wolfe, R.A., Messana, J.M. and Wheeler, J.R.C. Using knowledge of multiple levels of variation in care to target performance incentives to providers. Medical Care 2008, 46(2): 120-126
  19. Hirth, R.A., Turenne, M.N., Wheeler, J.R.C., Pozniak, A.S., Tedeschi, P., Chuang, C.C., Pan, Q., Slish, K. and Messana, J.M. Case-mix adjustment for an expanded renal prospective payment system. Journal of the American Society of Nephrology 2007, 18(9): 2565-2574
  20. Wheeler, J.R., Messana, J.M., Turenne, M.N., Hirth, R.A., Pozniak, A.S., Pan, Q., Chuang, C.C., Slish, K., Tedeschi, P., Roys, E.C. and Wolfe, R.A. Understanding the basic case-mix adjustment for the composite rate. American Journal of Kidney Disease 2006, 47(4): 666-671

NON-PEER REVIEWED  PUBLICATIONS

  1. Gastwirth, J.L., Modarres, R and Pan, Q.  Comment entitled “Some statistical aspects of the Department’s use of Cohen’s D in measuring differential pricing in anti-dumping cases that should be considered before it is formally adopted” submitted to International Trade Administration, Department of Commerce in response to their request for comments on Differential Pricing Analysis 79 FR 26720 [Docket No. 140318257-4257-01], 2014.
  2. Gastwirth, J.L. and Pan, Q.  Comments on “Qualifying the weight of evidence from a forensic fingerprint comparison: a new paradigm” by C. Neumann, I.W. Evett & J. Skerrett J. R. Statistic. Soc. A 2012, 175(2): 402-403.
  3. Pan, Q. Entry “Standard Error of Estimate” in Encyclopedia of Research Design edited by Neil Salkind,  2010, SAGE publications Inc: 1424-1427.
  4. Gastwirth, J.L. and Pan, Q.  Careful statistical reasoning can provide support for supreme court decision. AmStat News 2010, 397: 23-24.
  5. Sinclair, M.D. and Pan, Q. Response to Dr. Graubard. Law, Probability & Risk  2009, 8(2): 123-124.