Intermediate Biostatistics: Analysis of Discrete Data

This course provides a working knowledge of statistical methods suitable for data with discrete response values. Among the topics students will explore are statistics for contingency tables, Poisson and negative binomial regression, propensity scores, instrumental variables, principal components analysis, bootstrapping, cross-validation, and model building, all with an emphasis on epidemiologic applications. Students may use either R or SAS statistical software.

Topics Include

Univariate analyses of discrete data
Confounding and interaction
Mantel-Haenzel techniques
Logistic regression
Modeling predictors in logistic regression
Building hypothesis-driven models
Propensity scores
Building predictive models
Multinomial and ordinal logistic models
Regression for matched data: generalized estimating equation and conditional logistic

This course was formerly numbered HRP261