Chapter 6 Binary Logistic Regression

In this chapter, you will learn how to:

  • Interpret the equation for a binary logistic regression model;
  • Compute and interpret odds and odds ratios (OR);
  • Estimate unadjusted and adjusted ORs using binary logistic regression;
  • Interpret the estimated regression coefficients;
  • Create a forest plot to visualize estimated ORs and their confidence intervals;
  • Compute predicted probabilities from the model;
  • Test interactions between predictors;
  • Diagnose the fit of the model;
  • Appropriately summarize the methods and results for a binary logistic regression analysis;
  • Fit an ordinal logistic regression model;
  • Fit a conditional logistic regression model for matched case-control data; and
  • Fit a log-binomial regression model to estimate a risk ratio (RR) or prevalence ratio (PR).

To use the code in this chapter, first load tidyverse and Functions_rmph.R (downloadable from RMPH Resources).