The national shortage of biostatisticians continues to grow and new applications for biostatistics continue to increase. In 2010 the Bureau of Labor Statistics estimated that employment of statisticians would increase by 14% from 2010 to 2020, as fast as the average for all occupations. Some examples include (a) increased access to Big Data from health records to human genomics requiring biostatisticians to play a critical role in developing new analytical methods; (b) increased emphasis on clinical and translational research with an accompanying need for designing studies and testing the newly developed interventions; (c) the advent of precision medicine (patient tailored interventions) and associated small data design and analytic challenges; and (d) Patient Centered Outcomes Research requiring analytic support for comparative effectiveness research and causal analyses. To help address the shortage of biostatisticians, University of Texas Health Science Center School of Public Health (UTHSC-SPH) Department of Biostatistics seeks renewal of its successful NIGMS predoctoral T32 training grant in Biostatistics. The mission of the training program is to recruit, retain, and graduate well trained PhD Biostatisticians with an emphasis on training and mentoring underrepresented minorities from diverse scientific backgrounds. We plan to recruit three new pre-doctoral trainees per year to the program and provide each trainee with up to three years of support. Trainees will be early in their doctoral training with a mix of previous training in statistical or mathematical disciplines or biological sciences with some mathematical training. Trainees will be allowed up to three years of support. Since the training grant's inception in July 2006, 15 students have participated in the program, including 5 underrepresented minorities. Five students completed their PhDs, and an additional seven have passed their qualifying examinations and have started their dissertations. Trainees receive in-depth training in statistical theory and application to public health. Students choose additional electives from methods in high-dimensional data analysis i.e.,aspects of Big Data analytics, statistical genetics, and/or clinical trials methodology. Trainees must also take courses in a minor and breadth outside of Biostatistics. Within the UTHSC-SPH trainees have access to courses in genetics, epidemiology, behavioral sciences and/or health policy. Across the Health Science Center trainees can take courses in basic and clinical studies and in computing methods in bioinformatics. Trainees can obtain additional mathematical training at Rice University. Trainees obtain research experience within the Coordinating Center for Clinical Trials, the Human Genetics Center, University of Texas MD Anderson Cancer Center, the UTHSC medical center, other medical centers within the general Health Science Center, and can participate in summer interships with the National Aeronautics and Space Association (NASA), and pharmaceutical companies.

Public Health Relevance

This training program addresses the national shortage of biostatisticians, particularly underrepresented minorities, to work in medicine and public health. Biostatisticians develop new statistical tools to use the large amount of information available from such sources as medical imaging, human genetics, and electronic medical records. Biostatisticians design and conduct clinical trials to bring effective new treatments to patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
2T32GM074902-09A1
Application #
9074492
Study Section
Training and Workforce Development Subcommittee - D (TWD)
Program Officer
Marcus, Stephen
Project Start
2005-08-16
Project End
2019-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
9
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77225
Greene, Thomas J; DeSantis, Stacia M; Fox, Erin E et al. (2018) Utilizing Propensity Score Analyses in Prehospital Blood Product Transfusion Studies: Lessons Learned and Moving Toward Best Practice. Mil Med 183:124-133
Koslovsky, Matthew D; Swartz, Michael D; Chan, Wenyaw et al. (2018) Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation. Biometrics 74:636-644
Koslovsky, M D; Swartz, M D; Leon-Novelo, L et al. (2018) Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates. J Stat Comput Simul 88:575-596
Koslovsky, Matthew D; Hébert, Emily T; Swartz, Michael D et al. (2018) The Time-Varying Relations Between Risk Factors and Smoking Before and After a Quit Attempt. Nicotine Tob Res 20:1231-1236
Ma, Junsheng; Chan, Wenyaw; Tilley, Barbara C (2018) Continuous time Markov chain approaches for analyzing transtheoretical models of health behavioral change: A case study and comparison of model estimations. Stat Methods Med Res 27:593-607
Chang, Ronald; Fox, Erin E; Greene, Thomas J et al. (2017) Multicenter retrospective study of noncompressible torso hemorrhage: Anatomic locations of bleeding and comparison of endovascular versus open approach. J Trauma Acute Care Surg 83:11-18
Atem, Folefac D; Sampene, Emmanuel; Greene, Thomas J (2017) Improved conditional imputation for linear regression with a randomly censored predictor. Stat Methods Med Res :962280217727033
Rubin, Maria Laura; Chan, Wenyaw; Yamal, Jose-Miguel et al. (2017) A joint logistic regression and covariate-adjusted continuous-time Markov chain model. Stat Med 36:4570-4582
Holcomb, John B; Swartz, Michael D; DeSantis, Stacia M et al. (2017) Multicenter observational prehospital resuscitation on helicopter study. J Trauma Acute Care Surg 83:S83-S91
Benoit, Julia S; Chan, Wenyaw; Luo, Sheng et al. (2016) A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified. Stat Med 35:1549-57

Showing the most recent 10 out of 23 publications