140.755.01 ADVANCED METHODS IN BIOSTATISTICS V

Reviews the extension of linear models to generalized linear models. Includes exponential family models, link functions, and over-dispersion. Also introduces models and inferential methods for polytomous outcomes. Describes extension of models to account for clustering using explicit modeling via mixed effects framework and generalized estimating equations (GEE). Introduces methods and models for regression with covariates subject to measurement error. Describes and implements advanced computational algorithms, such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM).

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 Models for inference; 3) Describe models for polytomous outcomes; 4) Apply theoretical concepts to scientific data using R and WinBUGS software; 5) Conduct and interpret logistic, conditional logistic, and probit regression inference; 6) Extend models to account for clustering and correlation; 7) Introduce the mixed effects framework and describe its relationship to multilevel models; 8) Introduce models that account for measurement error in the covariates; 9) Provide new computational tools for complex models including Markov Chain Monte Carlo (MCMC) and Expectation Maximization (EM) algorithms; 10) Improve computational and analytic skills through analysis of simulated data sets.