BIOSTATS 6007EX - Categorical Data & Generalised Linear Models

The aim of this course is to enable students to use generalised linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians. The content includes: introduction to and revision of conventional methods of contingency tables especially in epidemiology; odds ratios and relative risks; chi-squared tests for independence; Mantel-Haenszel methods for stratified tables; and methods for paired data. The exponential family of distributions includes: generalised linear models (GLMs), and parameter estimation for GLMs; inference for GLMs-including the use of score, Wald and deviance statistics for confidence intervals and hypothesis tests, and residuals; binary variables and logistic regression models-including methods for assessing model adequacy; nominal and ordinal logistic regression for categorical response variables with more than two categories; and count data, Poisson regression and log-linear models.