Calculate Estimates in Models with Interaction.
interactionRCS
A tool to calculate and plot Hazard Ratios, Odds Ratios or linear estimates in a simple or restricted cubic splined interaction model
Version 1.1 (February 25, 2022)
Description
interactionRCS
facilitates interpretation and presentation of results from a regression model (linear, logistic, Cox) where an interaction between the main predictor of interest X (binary or continuous) and another continuous covariate Z has been specified. In particular, interactionRCS
allows for basic interaction assessment (i.e. log-linear/linear interaction models where a product term between the two predictors is included) as well as settings where the second covariate is flexibly modeled with restricted cubic splines. Confidence intervals for the predicted effect measures (beta, OR, HR) can be calculated with either bootstrap or the delta method. Lastly, interactionRCS
produces a plot of the effect measure over levels of the other covariate.
Installation
To install the latest version of interactionRCS
, type the following lines in a web-aware R environment.
if(!"devtools" %in% rownames(installed.packages())){
install.packages("devtools")
}
devtools::install_github("https://github.com/gmelloni/interactionRCS.git")
# or alternative devtools::install_git("https://github.com/gmelloni/interactionRCS.git")
library(interactionRCS)
Usage
After estimating a regression model (linear, logistic, Cox) such as model<-glm(y~ ...)
estimate and plot interactions with:
int<-estINT(model=model, ...)
plotINT(int, ...)
For a detailed introduction to interactionRCS
and code examples please refer to this vignette
Authors
Giorgio Melloni, Hong Xiong, Andrea Bellavia
TIMI study group, Department of Cardiovascular Medicine, Brigham and Womens Hospital / Harvard Medical School.