Biostatistics

DrPH students meet gain core and specific competencies of the MPH in Biostatistics and achieve additional competencies in the areas of data analysis and computing, public health and collaborative research, data management, teaching, and biostatistical research.

Upon satisfactory completion of the DrPH in Biostatistics, graduates will be able to:

Data Analysis and Computing

• Identify and implement advanced statistical models for the purposes of estimation, comparison, prediction, and adjustment in non-standard settings;

Public Health and Collaborative Research

• Describe the foundations of public health, including the biological, environmental, behavioral, and policy factors that affect the health of populations;
• 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;
• Evaluate research reports and proposals for research funding on the basis of their scientific integrity, validity, and the strength of the quantitative analysis;
• 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 data base 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

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, and data analytic techniques to public health students enrolled in first and second level graduate public health courses;
• Explain advanced concepts in the theory of statistical inference to graduate students in biostatistics and mathematical statistics;

Biostatistical Research

• Identify and integrate new developments in the statistical literature for challenging research problems in public health; and
• Generate original computer code for new statistical techniques.