MS in Medical Statistics
Other Available Graduate Programs
PhD in Statistics (traditional)
PhD in Statistics (bioinformatics concentration)
Master of Arts in Statistics
Program for the Degree of Master of Science in Medical Statistics
The M.S. Program in Medical Statistics is primarily intended for students who wish to follow careers in health-related professions such as those in the pharmaceutical industry and biomedical or clinical research organizations. The program consists of one core year (two semesters) of coursework as well as an internship/applied project (BST 493), which is normally taken in the summer after the core program. There are no thesis or language requirements. The degree requires 32 credit hours consisting of all the 400-level courses listed below; substitutions may be made with approval of the faculty program advisor.
The internship/applied project requirement is met by either completing a statistics internship in industry or by working with medical center investigators on an applied project. Students are required to write a formal report summarizing the findings from their research project. These findings are presented in a public lecture, which is followed by an oral qualifying examination. The student's M.S. committee will then determine the student's qualifications for the M.S. degree. For details on the applied project and oral examination, please refer to the M.S. Guidelines document.
The program is open to students with a substantial background in statistics. A bachelor's degree from an approved program is required.
Prerequisites: Three semesters of calculus, a course in linear algebra (similar to MTH 165), a course in probability (see STT 201), a course in mathematical statistics (see STT 203), and a course in applied statistics (similar to STT 212) are required.
A typical program for an entering student without previous advanced training is as follows:
Fall (12 credits)
- BST 411 Statistical Inference (4 credits)
- BST 430 Introduction to Statistical Computing (4 credits)
- BST 464 Applied Linear Regression (4 credits)
Spring (12 credits)
- BST 465 Design of Clinical Trials (4 credits)
- BST 466 Categorical Data Analysis (4 credits)
- Elective(s) selected from:
- BST 412 Large Sample Theory (4 credits)
- BST 413 Bayesian Inference (4 credits)
- BST 426 Linear Models (4 credits)
- BST 512 Topics in Statistical Inference
- BST 550 Topics in Data Analysis
- BST 570 Topics in Biostatistics
Summer (8 credits)
- BST 493 Internship/Applied Project (8 credits)