The Biostatistics Division in the Department of Population Health Sciences (PHS) is the home for biostatistics and research design expertise required for a wide range of preclinical research, epidemiological studies, and interventional biomedical trials. Our mission is to provide biostatistics education, promote accountability and rigor in research, develop statistical methodologies, and participate in research collaboration in clinical trials, epidemiological studies, and biomedical data science across UT Health San Antonio community.
Who we are and what we do
The faculty and staff at Biostatistics Division aim to establish a leading group of biostatisticians in the application and development of state-of-the-art and novel biostatistical methods. Our research areas include Causal analysis, latent class trajectory modeling, instrumental variable analysis, longitudinal outcomes, survival or time-to-event data, predictive analytics, epidemiological analysis, statistical genomics, and reproducible computing. By working with other PHS faculty from Bioinformatics, Clinical Informatics, and Research Informatics Divisions, we will provide a wide range of research collaboration and education partners. The collaboration includes, but not limited to:
Study design and data analysis of:
- Preclinical studies
- Clinical trials
- Large clinical databases
- Causal analysis
- Collaboration with investigators for study planning, clinical trial design, sample size, and power analyses; Support for conducting, monitoring, and reporting clinical trials and evaluation studies;
- Other data analytic needs in fundamental biomedical science, translational research, clinical and population-based research, including predictive analytics.
The Biostatistics Division, directed by Dr. Jonathan Gelfond, is an integral part of UT Health San Antonio’s Mays Cancer Center (NIH-NCI), Institute for the Integration of Medicine and Science (NIH-CTSA), and Claude D. Pepper Center for Healthy Aging (NIH-NIA). The Biostatistics Division enables, accelerates and catalyzes research, supports rigor, reproducibility, and accountability in data analysis, and provides biostatistics and data science education to graduate students and faculty.