Biostatistics

Students in the MS in Biostatistics degree program select one of five tracks of specialization: Accelerated Pre-Doctoral Training, Clinical Research Methods, Patient-Oriented Research, Statistical Genetics, and Theory & Methods. Whether the focus of the degree is to prepare for doctoral research training, to advance the skills critical for clinical scientists, to work in the field of human genetics or as a biostatistician in public health or the pharmaceutical industry, all MS in Biostatistics tracks require a facility for quantitative reasoning and a true enjoyment of working with data.

Competencies

The five specialty tracks differ in degree requirements and specific competencies, but each includes the core Biostatistics MS competencies in the areas of data analysis and computing, consulting, and public health and collaborative research.

Upon satisfactory completion of the Biostatistics MS degree, graduates will be able to:

Data Analysis and Computing

• Formulate and produce graphical displays of quantitative information (e.g., scatter plots, box plots, line graphs) that effectively communicate analytic findings;
• Explain general principles of study design in attempting to identify risk factors for disease, isolate targets for prevention, and assess the effectiveness of one or more interventions;
• Select and perform appropriate hypothesis tests for comparing two or more independent exposure groups, or two or more groups of matched/clustered subjects, with respect to a discrete or continuous response measurement of interest;
• Interpret associations estimated via linear regression, logistic regression, and Cox models for survival data;
• Apply the basic tenets of research design and analysis for the purpose of critically reviewing research and programs in disciplines outside of biostatistics;
• Interpret quantitative findings in accurate, accessible language for colleagues outside of biostatistics, as well as for broader dissemination to the public and other public health professionals;

Public Health and Collaborative Research

• Translate research objectives into testable hypotheses;
• Compare and contrast different study designs and their implications for inference in medical/public health research;
• Describe basic principles and the practical importance of key concepts from probability and inference (including random variation, systematic error, sampling error, measurement error, hypothesis testing, type I and type II errors, confounding bias, and effect modification) to colleagues without extensive statistical training;
• Develop and execute power and sample size calculations for research studies utilizing simple random sampling; and
• Evaluate research reports and proposals for research funding on the basis of their scientific integrity, validity, and the strength of the quantitative analysis.

Admissions to MS Programs

The particular requirements for admission may vary somewhat by track but applicants to the MS/Theory and Methods, MS Statistical Genetics, or MS/Accelerated Predoctoral Training tracks typically hold a bachelor’s degree in mathematical, biological, physical, or social sciences or will receive the degree before registering. Students from other backgrounds will be considered for admission as long as they have taken at least one year of college-level calculus (required) and a semester of linear algebra (which may be taken after enrollment, although this course will not count towards required degree credits).

Applicants to the MS/Clinical Research Methods and MS/Patient Oriented Research tracks should hold an advanced degree in a clinical discipline or public health.

The Mailman School of Public Health Admissions Office can provide more information on the application and deadline dates for the different tracks.

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