Evaluation of covariate effects using forest plots and introduction to the coveffectsplot R package
Abstract 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 o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12829 |
| Summary: | Abstract 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. |
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| ISSN: | 2163-8306 |