Evaluation of covariate effects using forest plots and introduction to the coveffectsplot R package

The current tutorial describes why forest plots are needed for an effective communication of covariates effects, how they are constructed, and how they should be presented. Simulation-based methodologies allowing the user to evaluate the marginal impact of changing one covariate at a time or by considering the joint effects of correlated covariates are introduced along with graphical tools for an optimal assessment of the covariate effects. The R package coveffectsplot and an associated R Shiny application are provided to facilitate the design and construction of forest plots for the visualization of covariate effects. All codes and materials are available on a public Github repository.

Automated summary

This tutorial explains the importance of forest plots in communicating covariate effects effectively, the construction methods, and presentation techniques. It also introduces simulation-based methodologies and graphical tools for evaluating covariate effects, along with providing an R package and application for designing and constructing forest plots.