Biostatistics

Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS in Biostatistics Theory and Methods Track provides students with both the skills necessary for a career as a biostatistician and the background needed for doctoral study.

In addition to achieving the MS in Biostatistics core competencies, students in the Theory and Methods Track gain the following specific competencies in the areas of public health and collaborative research, data management, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS in Biostatistics Theory and Methods Track, graduates will be able to:

Public Health and Collaborative Research

• Develop and execute calculations for power and sample size when planning research studies with complex sampling schemes;
• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Data Management

• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;
• Differentiate between analytical and data management functions through knowledge of the role and functions of databases, different types of data storage, and the advantages and limitations of rigorous database systems in conjunction with statistical tools;
• Describe the different types of database management systems, the ways these systems can provide data for analysis and interact with statistical software, and methods for evaluating technologies pertinent to both;
• Assess database tools and the database functions of statistical software, with a view to explaining the impact of data management processes and procedures on their own research;

Teaching Biostatistics

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and

Biostatistical Research

• Apply probabilistic and statistical reasoning to structure thinking and solve a wide range of problems in public health.