Intermediate Biostatistics: Analysis of Discrete Data


Stanford School of Medicine



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.

NOTE: This course updated its course number from HRP261


EPI259, formerly HRP259, or prior equivalent course background in statistics that included basic linear regression (with permission of the instructor)

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

Course Availability

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