The Division of Biostatistics in the University of Texas School of Public Health (UTSPH) seeks continuation of its training program in Biostatistics. The mission of the training program is to recruit, retain, and graduate well trained PhD Biostatisticians with an emphasis on training underrepresented minorities from diverse scientific backgrounds. The objectives of our program are to provide trainees with (i) in-depth training in statistical theory and methodologies, and biology, genetics, and/or clinical, epidemiologic, and behavioral sciences;(ii) exposure to a broad scope of research opportunities in basic and clinical studies;(iii) experience in conducting a research project;(iv) teaching experience;and (v) the opportunity to publish and present their work. We will recruit approximately 6 students currently in the Biostatistics program and 3 newly entering students in Year 1 and 3 newly entering students in subsequent years. Students will be a mix of (i) those who have no previous graduate training but have a degree in a statistical discipline and will likely require 3 years of support along with additional training in biological sciences and (ii) students with Master's degrees in Biostatistics or related discipline or in the biological sciences with demonstrated quantitative skills who will likely require two years of support. Students applying to Epidemiology with strong quantitative skills and an interest in genetics will be approached to determine their interest in pursuing a degree in Biostatistics if there are unfilled training grant positions available. Students will be funded for up to three years depending on their level of past education in Biostatistics. No more than 9 students will be participating per year over the five year period. The SPH Divisions outside of Biostatistics provide both coursework enhancing the training experience and research opportunities within the other Divisions'research centers. We are strengthening opportunities for research experiences with the near-by University of Texas MD Anderson Cancer Center, Baylor College of Medicine, and the University of Texas Houston Center for Clinical and Translational Sciences where our Dean is currently a co-investigator. The UTSPH's extremely successful interdisciplinary research programs and its location within the world's largest health science center produces broadly trained PhD biostatisticians with expertise in at least two additional areas beyond statistical theory and methodologies, such as biology, genetics, epidemiology, clinical trials and/or behavioral sciences. The demand for researchers with outstanding training in both biology and biostatistics is high given the recent rapid advances in biological research. Yet there is a national shortage of biostatisticians in general and of underrepresented minority biostatisticians in particular. This training program is geared to meet the challenge of preparing diverse 21st century biostatisticians who will be at the forefront of research and increasing minority representation in the profession.

Public Health Relevance

This program helps to address the national shortage of biostatisticians, particularly underrepresented minorities, with training in statistics and applications to medicine and public health. Biostatisticians are needed to help solve public health problems. As examples biostatisticians work on teams to study human genetics, design and conduct clinical trials, and study environmental change.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM074902-07
Application #
8496070
Study Section
Special Emphasis Panel (ZGM1-BRT-X (TR))
Program Officer
Brazhnik, Paul
Project Start
2005-08-16
Project End
2015-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
7
Fiscal Year
2013
Total Cost
$149,620
Indirect Cost
$8,490
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; H├ębert, 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

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