cureit: Cure Model Regression

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

Cure Model Regression

Description

Cure Model Regression

Usage

## S3 method for class 'formula'
cureit(
  surv_formula,
  cure_formula,
  data,
  conf.level = 0.95,
  nboot = 100,
  eps = 1e-07,
  ...
)

cureit(object, ...)

## Default S3 method:
cureit(object, ...)

Arguments

surv_formula	formula with Surv() on LHS and covariates on RHS.
cure_formula	formula with covariates for cure fraction on RHS
data	data frame
conf.level	confidence level. Default is 0.95.
nboot	number of bootstrap samples used for inference. Default number is 100.
eps	convergence criterion for the EM algorithm.
...	passed to methods
object	input object

Value

cureit object. The output includes the following:

• surv_coefs

• cure_coefs

• surv_formula

• cure_formula

• data

• conf.level

• nboot

• eps

• surv_xlevels

• cure_xlevels

• tidy

• smcure

• surv_blueprint

• cure_blueprint

• blueprint

See Also

Other cureit() functions: Brier_inference_bootstrap(), broom_methods_cureit, nomogram(), predict.cureit()

Examples

cureit_obj <- cureit(surv_formula = Surv(ttdeath, death) ~ age + grade, 
cure_formula = ~ age + grade,  data = trial, nboot = 10)

# pulling survival coeffients
cureit_obj$surv_coefs

# pulling cure coefficients
cureit_obj$coefs

# `tidy` object of survival model output
cureit_obj$tidy$df_surv

# `tidy` object of cure model output
cureit_obj$tidy$df_cure

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