140.756.01 ADVANCED METHODS IN BIOSTATISTICS VI

Reviews key topics in modern applied statistics. Extends the topics of 140.755 to encompass generalized linear mixed effects models and introduces nonparametric smoothing, functional data analysis and data mining. Includes extensions of linear mixed effects to discrete outcomes, nonlinear and multivariate smoothing, semi-parametric models for clustered data, and statistical learning techniques such as classification, decision trees, and boosting. Emphasizes both rigorous methodological development and practical data analytic strategies. Presents computational methods designed for semi-parametric inference and discusses relevant software.

Upon completion of this course, students will be able to: 1) Give examples of different types of data arising in public health studies; 2) Use modern statistical concepts such as Generalized Linear Mixed Models (GLMMs) for inference; 3) Describe the relationship between GLMMs and linear mixed models; 4) Extend models to account for clustering and correlation; 5) Introduce nonparametric smoothing models; 6) Describe modern statistical methods for complex datasets including functional data analysis and data mining; 7) Apply theoretical concepts to scientific data using R and WinBUGS software; 8) Improve computational and analytic skills through analysis of simulated data sets.