The Master of Science in Biostatistics is designed for students interested in applying statistical methods to the design and analysis of biomedical research and clinical investigations data.
The program focuses on the theory of biostatistics, data analytic methods, experimental design (including the design, conduct and analysis of clinical trials), statistical methods in human genetics, biomedical informatics and advanced statistical computing methods. Statistical methods are taught both from a practical and theoretical perspective.
MS in Biostatistics curriculum at-a-glance
Typically completed in 2 years, the 39-unit degree consists of 8 core courses (28 units), 2 to 5 elective courses (at least 7 units) and a master’s thesis (4 units). During the program, students take part in research teams assisting with study design, data coordination and management, statistical analysis and reporting of results.
Click courses for descriptions.
Concepts of biostatistics; appropriate uses and common misuses of health statistics; practice in the application of statistical procedures; introduction to statistical software including EXCEL, SPSS, nQuery.
Major parametric and nonparametric statistical tools used in biomedical research, computer packages including SAS. Includes laboratory.
Statistical methods for analysis of categorical data including dichotomous, ordinal, multinomial and count data, using Stata package. Includes laboratory.
Terminology/uses of epidemiology and demography; sources/uses of population data; types of epidemiologic studies; risk assessment; common sources of bias in population studies; principles of screening.
Statistical methods for analysis of various experimental designs. Parametric analysis of variance (ANOVA), repeated measures methods, crossover designs, non-parametric ANOVA.
Principles and methods used in epidemiology for comparing disease frequencies between groups. Restricted to the analysis of binary outcome variables.
Density distribution and hazard functions; normal, chi-square, student’s t and F distributions; and sampling procedures for single factor and multiple factor designs, distributions.
Theory of estimation and testing, inference, analysis of variance, theory of regression.
Recommended Preparation: college-level calculus and linear algebra.
The program culminates in a master’s thesis on a topic of the student’s choosing. Research consists of original work worthy of submission to a publication or peer review journal.
Past thesis titles
- Modeling Risk Factors for Early Smoking Experimentation in School-Aged Children
- The Effect of Vitamin D Supplementation on the Progression of Carotid IntimaMedia Thickness and Arterial Stiffness in Elderly African American Women: Results of a Randomized Placebo-Controlled Trial