UTH

Training Grants in Biostatistics 

Program Overview

The Training Grant Program in Biostatistics at UTHSC-SPH, funded through the National Institute of General Medical Sciences grant # T32GM074902, is designed to prepare Biostatistics predoctoral trainees for careers in biomedical sciences and public health research. This requires excellent training and mentorship in classical statistical theory, applied statistical analysis, and biomedical study design, as well as interdisciplinary training achieved through our new data science coursework that will allow trainees to meet the ongoing challenges of analyzing and integrating novel data types. The ultimate goal of the Training Grant Program in Biostatistics is to provide students with a breadth of education and experience as practicing biostatisticians and data scientists. The training award provides full funding for coursework required to complete the PhD program.

Training Director

Stacia DeSantis, PhD
Associate Professor
Department of Biostatistics and Data Science

Eligibility

Applicants must hold a bachelors degree from an accredited college or university, be a US citizen or permanent resident, and meet all eligibility requirements of the Biostatistics and Data Science Program.

In addition to requirements for the PhD in Biostatistics and Data Science, including PHM1320 Health Ethics, training grant specific requirements are:

  • Attendance at monthly University of Texas MD Anderson Responsible Conduct of Research seminar series.
  • Attendance at bimonthly meetings with the training grant Director who is also the Academic Coordinator.
  • Engagement in a mentored structured research project 8 hours per week with the expectation of hands-on methodologic research and data analysis that will lead to peer-reviewed publications.

Current trainees

Derek Brown

Derek is a first year PhD Biostatistics student at the Houston campus. He received a BS in Mathematics from The University of Kansas and a MS in Biostatistics from The George Washington University. After he finished his MS degree, he worked for two years at the DoD Cancer Center in Bethesda Maryland. He is interested in cancer research and survival analysis.

Elysia Garcia

Elysia Garcia is a Ph.D. student within the Department of Biostatistics at the University of Texas Health Science Center in Houston. She earned a Bachelor of Science in Statistics from Baylor University and graduated from the Baylor University Honors College in 2014. After completing her undergraduate degree, Ms. Garcia continued her education by completing a Master of Public Health in Biostatistics from the University of North Texas Health Science Center, from which she graduated in 2016. Ms. Garcia has worked as a data analyst and statistical consultant for various medical organizations. She has also led research projects utilizing Item Response Theory to identify health behavior indicators as well as an opiate dosage utilization study within the Department of Veteran Affairs Valley Coastal Bend Health Care System.

Abigail Sedory

Abigail Sedory is a first year PhD Biostatistics students at the University of Texas Health Science Center School of Public Health in Houston. She earned a Bachelor of Science degree in Biology with a minor in Mathematics from Southwestern University in Georgetown, Texas. After finishing her undergraduate degree program, Abigail became a certified high school mathematics teacher and taught for a year before pursuing a Master of Science degree in Biostatistics. She received her M.S degree in Biostatistics from the University of Texas Health Science Center School of Public Health where she focused on intervention research analysis within the Health Promotions/Behavioral Science department. She is also interested in Genetics research.

Bin Shi

Bin Shi is a third year PhD Biostatistics student at the Houston campus. He received a MD, MS and PhD in China after which he worked in cancer related research during which his interest in Biostatistics was ignited. He plans to continue cancer related biostatistics research.

Training grant outcomes

Time to completion of PhD

The Training Grant Students who have entered the program have completed their doctoral degrees within 4 to 7 years. The average time to completion of the degree has been 4.5 years. Those who enter the program without a master’s degree in biostatistics typically take 5.5 years to complete the program. There are currently 4 students appointed to the training grant.

Graduation rate of Training Grant students

Of students who entered the program at its initiation in 2010, 100% completed their PhD. As data for later years are incomplete due to students’ continued enrollment in the program, the updated graduation rate will be forthcoming within one year.

A sample of careers obtained by Training Grant students

Our Training Grant students have gone on to both research and non-research careers in academia, government entities, and industry. Others have continued training via post doctoral appointments in Biostatistics. A sample of careers of our Training Grant graduates include:

  • Assistant Professor, Texas Institute for Measurement, Evaluation, and Statistics, University of Houston
  • Mathematical Statistician, US Food and Drug Administration
  • Post-Doctoral Fellow, Department of Biostatistics, MD Anderson Cancer Center
  • NASA Flight and Medical Operations, Biostatistician
  • Quintile Corporation, Biostatistician

How to apply

Eligible individuals must complete the standard application for the PhD program in Biostatistics and Data Science. Additionally, applicants must submit a one page statement indicating their interest in the Training Grant Program in Biostatistics and how this program will enhance their educational and career goals. Please submit this statement along with your application.

Department Specific Admission Requirements

  • Bachelor’s degree in mathematics or statistics or MS degree in the theory and applications of biostatistics, mathematics, statistics or equivalent is required
  • Requires calculus and linear algebra
  • Satisfactory score on GRE
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