At the Colorado School of Public Health you can train in the high-demand field of biostatistics on the region's premier health sciences campus - the University of Colorado Anschutz Medical Campus.
Offered through the CU Denver Graduate School and Colorado School of Public Health, the master of science in biostatistics exposes students to a wide variety of studies and research interests including longitudinal data, statistical genetics and genomics, clinical trials, infectious disease, and cancer research.
The program targets students with strong skills and training in mathematics with the interest to apply math in health care and biological settings. Students pursuing the MS look forward to rewarding careers in academia, research, public health and private industry. Potential positions include design and analysis of health studies, clinical trials, drug development and public health studies.
Coursework includes: applied and theoretical statistics; consulting; analysis of clinical trials; longitudinal and survival data; as well as production of a research paper or thesis. Please note this program requires a year of recent calculus with a minimum grade of B+ or higher. Students interested in a less mathematical program with broader exposure to Public Health should consider the MPH in Applied Biostatistics.
|Required Biostatistics Courses (20 credits)
||Biostatistical Methods I
||Biostatistical Methods II
||Biostatistical Consulting I
||Biostatistical Consulting II
||Advanced Data Analysis
||Statistical Theory I
||Statistical Theory II
||Longitudinal Data Analysis
|Required Public Health Courses (6 Credits)
||Foundations in Public Health
|Elective Biostatistical Courses (5 credits from this list)
(courses not listed require director approval)
||Design of Studies in Health Sciences
||Stat. Methods in Genetic Association Studies
||Analysis of High-throughput Data
|Total Program Credits
(Curriculum is subject to change annually)
- Study development: Work collaboratively with biomedical or public health researchers and PhD biostatisticians, as necessary, to provide biostatistical support for development and design of research studies.
- Map study aims to testable statistical hypotheses.
- Identify the strengths and weaknesses of various clinical trial and observational study designs and the data collection methods that go with these designs.
- Use probability and statistical theory to develop appropriate data analysis plans for study hypotheses.
- Modeling and Analysis: Develop, carry out and report biostatistical modeling analysis of biological science and public health studies.
- Use summary and graphical methods to carry out exploratory data analyses for data examination.
- Use probability and statistical theory to identify appropriate modeling and analysis methods to address study hypotheses.
- Use computer software for data management and for summarizing, analyzing and displaying research results.
- Determine and check modeling assumptions, and verify validity of proposed analyses.
- Carry out valid and efficient modeling, estimation and inference to address study hypotheses, using standard statistical methods including basic one and two sample methods, general linear models including regression and ANOVA, logistic regression, and clustered and longitudinal analysis.
- Read biostatistical literature to determine and implement alternate methods of analysis.
- Biologic or Public Health Relevance: Show how biostatistical tools apply to and influence research and policy development in the biomedical and public health arenas.
- Read subject specific biomedical or public health literature and synthesize issues that are important in the design, implementation, and analysis of research in the subject area.
- Carry out specialized analyses in biological (e.g. genetic association, microarray) or public health (e.g. epidemiological) settings.
- Communication: Communicate orally and in writing biostatitical concepts and results to both biostatistical and non-biostatistical audiences.
- Communicate orally and in writing simple and complex statistical ideas and methods to collaborators in non-technical terms including preparation of analysis section of grant proposals and methods and results sections of manuscripts.
- Manage the preparation of large documents (e.g. grant proposals or manuscripts).