Qi Long, Ph.D.

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Professor of Biostatistics in Biostatistics and Epidemiology
Department: Biostatistics and Epidemiology
Graduate Group Affiliations

Contact information
Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine
University of Pennsylvania
201 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104
Office: 215-573-0659
Fax: 215-573-1050
Education:
B.S. (Biochemistry)
School of Gifted Young, University of Science and Technology of China, Hefei, Anhui, China, 1998.
M.S. (Biostatistics)
University of Michigan, Ann Arbor, MI, 2003.
Ph.D. (Biostatistics)
University of Michigan, Ann Arbor, MI, 2005.
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Description of Research Expertise

Dr. Long’s research purposefully includes novel statistical research and impactful biomedical research, each of which reinforces the other. Its thrust is to develop statistical and machine learning, bioinformatics, and data mining methods for advancing precision medicine and population health with keen interests in big health data including, but not limited to, -omics data, electronic health records (EHRs) data, and mobile health (mHealth) data. Specifically, he has developed methods for analysis of big biomedical data (-omics, EHRs, and mHealth data), predictive modeling, missing data, causal inference, Bayesian methods and clinical trials. He also has made significant contributions to biomedical research areas such as cancer, cardiovascular diseases, diabetes, mental health and stroke.

Dr. Long’s research has been supported by the National Institutes of Health, the Patient-Centered Outcomes Research Institute, the National Science Foundation, the U.S. Department of Veterans Affairs and the American Heart Association.

Dr. Long is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently directs the Biostatistics Core in the Abramson Cancer Center at the University of Pennsylvania.

Selected Publications

Min, E.J.*, Safo, S.E., and Long, Q.: Penalized Co-Inertia Analysis with Applications to -Omics Data. Bioinformatics 35(6): 1018-1025, 2018 Notes: *mentee.

Zhao, Y.*, Chung, M., Johnson, B.A., Moreno, C.S., and Long, Q.: Hierarchical feature selection incorporating known and novel biological information: Identifying genomic features related to prostate cancer recurrence. Journal of the American Statistical Association 111(516): 1427-1439, 2016 Notes: * mentee (An earlier version won Yize Zhao the David P. Byar Travel Award from American Statistical Association’s Biometrics Section 2014).

Chang, C.*, Kundu, S., and Long, Q: Scalable Bayesian variable selection for structured high-dimensional data. Biometrics 74(4): 1372-1382, 2018 Notes: *mentee.

Safo, S.E.*, Li, S., and Long, Q.: Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information. Biometrics 74(1): 300-312, 2018 Notes: *mentee.

Sun, W.*, Chang, C.*, Zhao, Y., and Long, Q. : Knowledge-guided Bayesian Support Vector Machine for High-Dimensional Data with Application to Genomic Data. 2018 IEEE International Conference on Big Data (IEEE BigData 2018) Page: 1484-1493, 2018 Notes: *mentee.

Long, Q., Xu, J., Osunkoya, A.O., Sannigrahi, S., Johnson, B.A., Zhou, W., Gillespie, T., Park, J.Y., Nam, R.K., Sugar, L., Stanimirovic, A., Seth, A.K., Petros, J.A., and Moreno, C.S.: Global transcriptome analysis of formalin-fixed prostate cancer specimens identifies biomarkers of disease recurrence. Cancer Research 74(12): 3228-3237, 2014.

Long, Q., Johnson, B.A., Osunkoya, A.O., Lai, Y., Zhou, W., Abramovitz, M., Xia, M., Bouzyk, M., Nam, R., Sugar, L., Stanimirovi, A., Leyland-Jones, B.R., Seth, A.K., Petros, J.A., Moreno, C.S.: Protein-coding and microRNA biomarkers of recurrence of prostate cancer following radical prostatectomy. American Journal of Pathology 179(1): 46-54, 2011.

Clifton, S.M., Kang, C., Li, J., Long, Q., Shah, N. and Abrams, D.M.: Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain. Journal of Computational Biology 24(7): 675-688, 2017.

Zhao, Y., and Long, Q.*: Variable Selection in the Presence of Missing Data: Imputation-based Methods. Wiley Interdisciplinary Reviews: Computational Statistics 9(5): e1402, 2017 Notes: *Corresponding author.

Deng, Y.*, Zhang, X. and Long, Q.: Bayesian modeling and prediction of patient accrual in multi-regional clinical trials. Statistical Methods in Medical Research 26(2): 752-765, Aug 2017 Notes: * mentee (An earlier version won the third place in the ASA Biopharmaceutical Section Poster Competition at the 2015 Joint Statistical Meetings).

Zhao, Y*. and Long, Q.: Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research 25(5): 2021-2035, Oct 2016 Notes: * mentee.

Long, Q., Little, R.J., and Lin, X.: Causal inference in hybrid intervention trials involving treatment choice. Journal of the American Statistical Association 103(482): 474-484, 2008.

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Last updated: 05/09/2019
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