Public Health PhD: Biostatistics PHD
The biostatistics PhD builds on the classic public health biostatistics skills and knowledge base and takes advantage of the special knowledge of its faculty in the areas of genetics, bioinformatics and big data science.
Students have the opportunity to learn and apply statistical genetics in the context of complex disease study, high-throughput computing used in big data science and applications in evidence-based, patient-centered outcome studies.
Liquid biopsies could lead to better cancer treatments
Liquid biopsies offer a way to search for cancer cells in the blood and other bodily fluids, perhaps before symptoms appear. They hold the promise of a less-intrusive alternative to traditional surgical biopsies that also provides better cancer detection and treatment.
Chiang-Ching (Spencer) Huang, a professor of biostatistics in the Joseph J. Zilber College of Public Health, uses big data to study the potential of liquid biopsies. “We want to know how accurate tests are in detecting cancer cells in the blood,” he says.
Biostatistics PhD coursework includes topics and material such as interpretation of personalized and evidence-based medicine in the context of public health; basic understanding of genetics and epigenetics; and general omic approaches and concepts.
Public Health PhD: Biostatistics
Minimum degree requirement is 60 graduate credits beyond the bachelor’s degree (plus an additional 9 credits dedicated to dissertation writing and research), at least 35 of which must be earned in residence at UWM. The student, in consultation with the major professor, must create a plan of study and submit to the Biostatistics Faculty by the end of the first year. Minimum course requirements for all work requires approximately two to three full years of study.
Required Core PhD Courses (12 credits)
|PH 704 Principles and Methods of Epidemiology||3|
|PH 711 Intermediate Biostatistics||3|
|PH 801 Seminar in Public Health Research||3|
|PH 819 Social and Environmental Justice in Public Health||3|
Required Methods Courses, 30 credits
|MTHSTAT 871 Mathematical Statistics I||3|
|MTHSTAT 872 Mathematical Statistics II||3|
|MATH 768 Applied Stochastic Processes||3|
|Or (not both) MATH 883 Theory of Probability|
|PH 718 Data Management and Visualization in R||3|
|PH 813 Practice of Biostatistical Consulting||3|
|PH 818 Statistical Computing||3|
|PH 911 Generalized Linear Models||3|
Electives, at least 27 credits
|PH 714 Statistical Genetics and Genetic Epidemiology||3|
|PH 715 Applied Categorical Data||3|
|PH 716 Applied Survival Analysis||3|
|PH 717 Applied Longitudinal Data Analysis||3|
|PH 720 Special Topics in Biostatistics||1-3|
|PH 721 Introduction to Translational Bioinformatics||3|
|PH 723 Design, Conduct and Analysis of Clinical Trials||3|
|PH 758 Social Epidemiology||3|
|PH 762 Environmental Epidemiology||3|
|PH 768 Cancer Epidemiology||3|
|PH 769 Critical Perspectives on Nutritional Epidemiology and the Food System (TBD)||3|
|PH 811 Causal Inference||3|
|PH 812 Statistical Learning & Data Mining (3 credits)||3|
|COMPSCI 708 Scientific Computing||3|
|COMPSCI 711 Introduction to Machine Learning||3|
|BIO SCI 597 RNA Structure, Function, and Metabolism||3|
|BIO SCI 490 Molecular Genetics||3|
|MTHSTAT 564 Time Series Analysis||3|
|MTHSTAT 565 Nonparametric Statistics||3|
|MATH 768 Applied Stochastic Processes||3|
|MTHSTAT 863 Hypothesis Testing||3|
|MTHSTAT 869 Advanced Topics in Mathematical Statistics||3|
Doctoral Thesis, at least nine credits
PH 990 Research and Dissertation (1-8 credits, repeatable)
Please note: All courses subject to change. Please consult the Academic Catalog for the most up-to-date information.
Upon graduation, a student completing the requirements for the PhD in public health with a concentration in biostatistics will be able to:
PhD Core Competencies
- Formulate and test a hypothesis using basic statistical methods.
- Apply statistical inference to guide research decision-making relevant to public health problems and issues.
- Critically evaluate scientific literature and identify how epidemiological and population health data can be used to answer research questions and inform program development and policy decisions aimed at promoting health equity.
- Demonstrate critical thinking skills necessary for formulating research questions, identifying theory to frame research questions, and identify and employ appropriate methodologies for addressing a public health research question.
- Apply social and environmental justice framework when asking and addressing research questions impacting the public’s health.
Biostatistics Program Competencies
- Develop new statistical methodologies to solve problems in biomedical, clinical, public health or other fields.
- Contribute to the body of knowledge in the field of biostatistics by writing and successfully submitting manuscripts for publication in a peer-reviewed journal.
- Perform all responsibilities of a statistician in collaborative research, in particular design studies, manage and analyze data, and interpret findings from a variety of biomedical, clinical or public health experimental and observational studies.
- Communicate statistical information effectively with individuals with varying degrees of statistical knowledge through written and oral presentations.
- Use statistical, bioinformatic and other computing software to organize, analyze and visualize data.
- Review and critique statistical methods and interpretation of results in published research studies, presentations or reports.
- Demonstrate solid theoretical knowledge necessary for the development and study of new statistical methods.
- Understand and implement modern statistical approaches emerging in the literature to improve biomedical and public health.
Biostatisticians go on to work in hospitals, for health insurance systems, pharmaceutical companies, companies producing health-related products or health nonprofits, among other opportunities.
Biostatistics Faculty Expertise
- Genetic determinants of common chronic diseases, including heart disease, bleeding disorders, type II diabetes, stroke and colorectal cancer.
- Using genomic technologies and bioinformatic and biostatistical techniques to accurately predict risk and treatment response in cancer and cardiovascular disease.
- Major molecular mechanisms and pathways that modulate disease progression.
- Using biomedical informatics, mathematical modeling and simulations to characterize and predict the use of genetics in medical practice and, in particular, pathology.
- Use of high-throughput genetic technologies, such as micro-arrays and next-generation sequencers in the discovery and applications of genetics to complex diseases and environmental gene development pathways.
- Statistical methods and computational tools to identify genetic variants that influence the susceptibility to complex diseases, such as cancer of the breast, colon/rectum, lung and prostate.