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).