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The goal of this course is to introduce some statistical modeling approaches, which have been developed in the last few years and are widely used in medical and public health research, but are not covered in the core courses of the MS/PhD programs in Biostatistics and Epidemiology. Topics include penalized regression, methods for classification, evaluation of predictions, and robust regression. The cross-validation and bootstrapping procedures, which are important in evaluating and performing inference for models, will be introduced.

Integration of linear models with experimental design and sampling, considering applications with unbalanced and missing data. In-depth discussion of mixed models including parameterizations, analysis of covariance, unequal numbers of observations per cell, missing cells. Repeated measure designs and longitudinal data analysis emphasized, with many examples illustrated using SAS. Prerequisite: BIOST&EP 640 and 691F or equivalent. Recommended: Bioepi 741 and 744.

Principles of statistics applied to analysis of biological and health data. Continuation of Bioepi 540 including analysis of variance, regression, nonparametric statistics, sampling, and categorical data analysis.

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This course offers a course-based undergraduate experience (CURE) for students who are enrolled in STEM majors. Students will develop skills in STEM research, communication and academics. We will use the general biology topics to focus our efforts as we 1) Sharpen our skills in reading and understanding the relevant popular and scientific literature, 2) learn how to design and carry out an original team-based research project, 3) develop the analytical skills required to analyze our data, and 4) improve our skills in communicating complex scientific concepts to scientists and non-scientists.