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
Philadephia, PA 19104
Office: 215-573-0659
Fax: 215-573-1050
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 the use of big biomedical 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

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).

Hu, Y.J., Schmidt, A.F., Dudbridge, F., Holmes, M.V., Brophy, J.M., Tragante, V., Li, Z., Liao, P., McCubrey, R., Horne, B., Hingorani, A., Asselbergs, F.,* Patel, R.,* and Long, Q.* on behalf of the GENIUS-CHD Consortium: The impact of selection bias on estimation of subsequent event risk. Circulation: Genomic and Precision Medicine 10(5): e001616, 2017 Notes: *joint senior authors.

Li, Z.*, Safo, S.E., and Long, Q.: Incorporating biological information in sparse principal component analysis with application to genomic data. BMC Bioinformatics 18(1): 332, 2017 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.

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.

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.

Wang, M.* and Long, Q. : Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic. Biometrics 72(3): 897-906, Jan 2016 Notes: * mentee (An earlier version won Ming Wang the 2016 Junior Faculty Presentation Award from the Association for Clinical and Translational Statisticians).

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.

Hammadah, M., Al-Mheid, I., Wilmot, K., Ramadan, R., Abdelhadi, N., Alkhoder, A., Obideen, M., Pimple, P., Levantsevych, O., Kelli, H.M., Shah, A., Garcia, E.V., Sun, Y., Pearce, B., Kut- ner, M., Long, Q., Ward, L., Ko, Y., Mohammed,K., Blackburn, E., Zhao,J., Lin, J., Bremner, J.D., Kim, J., Edmund Waller, E., Raggi, P., Sheps, D., Quyyumi, A.A., and Vaccarino, V.: Telomere shortening, regenerative capacity, and cardiovascular outcomes. Circulation Research 120(7): 1130-1138, 2017.

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Last updated: 05/14/2018
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