BIO Spring 1 Courses

BIO8000 Capstone II MS Biostatistics

Credits: 1 Offered: Spring 1

The Capstone is a required three-semester course for students in the MS in Biostatistics Program. It provides experience in the art of consulting and in the proper application of statistical techniques to clinical and translational research. Students will bring together the skills they have acquired in previous coursework and apply them to the consulting experience. Learning will take place by doing. In the Spring I term, students will start participating in real-life consultations and reporting in class about their progress. Prerequisites: Must be enrolled in the MS in Biostatistics program.

BIO8200 Analysis of Categorical Data

Credits: 3 Offered: Spring 1

This course provides a comprehensive overview of methods of analysis for binary and other discrete response data, with applications to epidemiological and clinical studies. It is a second level course that presumes some knowledge of applied statistics and epidemiology. Topics discussed include 2 x 2 tables, m x 2 tables, tests of independence, measures of association, power and sample size determination, stratification and matching in design and analysis, inter-rater agreement, logistic regression analysis. Prerequisites: BIO6400 or MPH0300 and BIO6100 or MPH0400

BIO8500 Probability and Inference II

Credits: 3 Offered: Spring 1

This course is the second part of a two course sequence in Probability and Inference which follows Probability and Inference I. Statistical inference is the theoretical foundation for statistical methods used in the biological sciences. Essential topics covered in this course include: point estimation, confidence sets, the likelihood function, and statistical hypothesis testing. Optimality criteria for estimation and testing are developed. Other topics to be discussed include basic notions from Bayesian inference and Decision Theory as well as the theory of linear models. Nonparametric inference and other areas may be included as time permits. Prerequisites: BIO6400 and BIO6500

BIO8700 Theory of Linear & Generalized Linear Models

Credits: 3 Offered: Spring 1

This course provides a comprehensive overview of regression methods for analysis of continuous (normally distributed) and categorical (binary and count) data. The aim of this course is to provide a systematic training in both the theoretical foundations and the model building strategies of generalized linear models for MS/MPH and PhD students who have already had some data analysis experience. The course covers the theoretical background underlying regression techniques. Topics discussed include simple linear regression, multiple linear regression and Analysis of Variance (ANOVA) techniques for normally distributed data, as well as Poisson regression, log linear models and negative binomial regression for categorical data. Also regression diagnostics and Power and Sample size determination applied to these models. Prerequisites: BIO6400, BIO6500 as well as coding skills in either SAS or R.