In karissawhiting/cure-pipeline: A package to build and evaluate cure models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

Cureit

This package allows you to easily fit and report results from cure mixture models using a tidy framework. The package includes functions to:

• Fit mixture cure models
• Summarize coefficients using tidiers and gtsummary
• Vizualize the data using nomograms
• Assess model results with Brier scores and K-indicies

Installation

You can install {cureit} with the following code:

remotes::install_github("karissawhiting/cureit")

Load the package:

library(cureit)

Fit Mixture Cure Models

Functions to fit the models are wrappers for the smcure() function from the {smcure} package with the additional capability of passing a formula and directly passing categortical variables without first creating a model matrix:

p <- cureit(surv_formula = Surv(ttdeath, death) ~ age,
   cure_formula = ~ age,
   data = trial)

p$surv_coefs

p$cure_coefs

Contributing

Please note that the cureit project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Thank you to Sabrina Lin (@stl2137) for package contributions!

karissawhiting/cure-pipeline documentation built on Aug. 18, 2024, 1:22 a.m.