The Division of Biostatistics at the University of Texas Health Science Center at Houston (UTHSCH) will establish a pre-doctoral training in Biostatistics, by strengthening the links between the PhD degree programs in Biostatistics and the Human Genetics center. We will use the immense opportunities afforded by the School of Public Health (SPH), the Coordinating Center for Clinical Trials, the Human Genetics Center, the Center for Health Promotion and Prevention Research and the Graduate School of Biological Sciences, among other entities within our institution. The UTHSCH-SPH's extremely successful interdisciplinary research programs and location within the world's largest health science center produces broadly trained PhD biostatisticians with interdisciplinary expertise in at least two additional areas beyond statistical theory and methodologies, such as biology, genetics, epidemiology, clinical trials and behavioral sciences. The demand for researchers with outstanding training in both biology and biostatistics is high and more evident given the recent rapid advances in biological research. The Division of Biostatistics clearly recognizes this trend and this training program is geared to meet the challenge of preparing 21st century biostatisticians who will be at the forefront of research. This application seeks support for a formal training program in Biostatistics of 18 trainees during the period of five years with a focus in statistical genetics. The objectives of the training program are (i) provide in-depth training in statistical theory and methodologies, and biology, genetics, clinical, epidemiological and behavioral sciences, (ii) provide exposure and a broad scope of research opportunities in basic and clinical studies by our faculty, seminars and journal clubs (iii) recruit, retain and graduate 18 PhD Biostatisticians from diverse scientific backgrounds as well as underrepresented minorities with interdisciplinary research training and (iv) establish a home for students of diverse backgrounds with the common interest to apply statistical and computational approach to biological research. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
1T32GM074902-01A1
Application #
7122591
Study Section
Special Emphasis Panel (ZGM1-BRT-6 (BS))
Program Officer
Gaillard, Shawn R
Project Start
2006-07-14
Project End
2011-06-30
Budget Start
2006-07-14
Budget End
2007-06-30
Support Year
1
Fiscal Year
2006
Total Cost
$106,550
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Type
Other Domestic Higher Education
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77225
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
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
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