Track, Predict, and Improve Health Outcomes through Mathematical Modeling
Mitigate the causes and consequences of medical, pharmaceutical, and other public health problems by designing studies that model and interpret large-scale biological data.
The PhD in Biostatistics prepares you to succeed in academic research positions and leadership roles in health-related data analysis within the public or private sector.
Deepen your statistical expertise, gain college teaching experience, and complete a major research project that advances the discipline and helps improve the health of countless people.
Program of Study
The biostatistics doctorate requires you to earn 60 credits and complete a dissertation. For the first two years, you follow the curriculum for the master’s in biostatistics program. After that, you work with your faculty advisor to develop a specialization through your remaining coursework. Finally, you design a biostatistical study in your area of interest and report your findings in a publishable report.
Learn first-hand from faculty who actively address public health issues facing New York State (NYS). More than 75% of faculty in the School of Public Health hold appointments with the NYS Department of Health, which provides many research opportunities in the labs of the internationally recognized Wadsworth Center.
You may also work with biotechnology firms located on UAlbany’s Health Sciences Campus and with faculty in the University’s Cancer Research Center, Prevention Research Center, Public Health Preparedness Center, Cardiac Quality Improvement Initiative, and Center for Health Workforce Studies.
Biostatistics students frequently work on research projects related to vector-borne diseases, human immunodeficiency virus, sexually transmitted diseases, hospital admissions, vital records, toxicology, outbreak investigations, chronic diseases, nutrition, and environmental and occupational epidemiology.
The PhD program in Biostatistics prepares you for employment in multiple settings including universities, hospitals, public health and other government agencies, nonprofit organizations, NGOs, and health care and pharmaceutical companies.
Alumni have obtained academic research and teaching positions at Duke University, Pennsylvania State University, John Hopkins University, and Harvard University.
Other doctorate students pursue jobs with titles like: nutritional epidemiology statistician, pharmacogenomics statistical analyst, health economist, geospatial statistician, clinical research analyst, disease control specialist, medical research biostatistician, and quantitative health analyst.
Learning objectives that UAlbany students are expected to attain through their course of study within their academic program.
- Describe the roles biometry and statistics serves in the discipline of public health.
- Describe basic concepts of probability, random variation and commonly used statistical probability distributions.
- Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
- Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
- Apply descriptive techniques commonly used to summarize public health data.
- Apply common statistical methods for inference.
- Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
- Apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation.
- Interpret results of statistical analyses found in public health studies.
- Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences.
- Capability to build statistical model over real health data.
- Estimate and compare efficiency of models.
- Use statistical software to analyze health –related data.
- Be able to explain the advantages of a Bayesian data analysis.
- Perform univariate data analysis for continuous and categorical variables.
- Interpret inferential findings within Bayesian thinking (e.g. credible intervals, hypothesis testing).
- Conduct inference via posterior simulation and simulations tool.