Jing Qian

Jing Qian

Associate Professor of Biostatistics

Survival analysis under complex sampling; quantile regression; treatment of censored covariates; biomarker evaluation and risk prediction; semiparametric and nonparametric inferences; analytic methods for high-dimensional data

Location

Arnold House

715 North Pleasant Street
Amherst, MA 01003
United States

430 Arnold House

Jing Qian’s research interests include survival analysis under complex sampling, in which he develops estimation and hypothesis testing methods under censoring and dependent truncation arising from observational studies. By developing quantile regression approach for time-to-event outcomes under complex sampling design, Qian introduces flexible modeling methods to deal with non-constant biomarker effects in cancer epidemiological studies. Currently, he is also active in developing statistical methods for covariates subject to censoring, risk prediction under complex sampling design, high-dimensional variable selection, and prediction for large-scale imaging or genetic biomarkers.

Qian’s main areas of collaborative research include Alzheimer's disease studies and cancer epidemiology.

Education

  • Postdoctoral, Harvard University, 2009-2011
  • PhD, Emory University, 2009
  • BS, Renmin University of China, 2002

Key Publications

Qian, J. and Peng, L. (2010). Censored quantile regression model with partially functional effects. Biometrika, 97 (4), 839-850.

Qian, J., Payabvash, S., Kemmling, A., Lev, M.H., Schwamm, L.H., and Betensky, R.A. (2014) Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients. Biometrics, 70, 153-163.

Qian, J., Nunez, S., Kim, S., Reilly, M.P., and Foulkes, A.S. (2017) A score test for class-level association with non-linear biomarker trajectories. Statistics in Medicine, 36(19), 3075-3091.

Qian, J., Hyman, B.T. and Betensky, R.A. (2017) Neurofibrillary tangle stage and the rate of progression of Alzheimer symptoms: Modeling using an autopsy cohort and application to clinical trial design. JAMA Neurology, 74(5), 540-548.

Qian, J., Chiou, S., Maye, J. E., Atem, F., Johnson, K.A. and Betensky, R.A. (2018+) Threshold regression to accommodate a censored covariate. Biometrics, In press.