Cure Models for Long-term Survivors — curemodels • ClinicoPath

Cure models for analyzing survival data with a cured fraction of long-term survivors. Implements mixture cure models and non-mixture cure models for oncology research.

Usage

curemodels(
  data,
  time,
  status,
  predictors,
  model_type = "mixture",
  cure_link = "logit",
  survival_dist = "weibull",
  bootstrap_ci = FALSE,
  n_bootstrap = 1000,
  cure_threshold = 60,
  plot_cure_fraction = FALSE,
  plot_survival = FALSE,
  goodness_of_fit = FALSE,
  sensitivity_analysis = FALSE,
  use_background_mortality = FALSE,
  background_hazard_var = NULL,
  npcure_covariate = NULL,
  npcure_bandwidth = "auto",
  npcure_time_points = 100
)

Arguments

data: The data as a data frame.
time: Follow-up time variable
status: Event status variable
predictors: Predictor variables for the model
model_type: Type of cure model: mixture (smcure), non-mixture (flexsurvcure), cuRe, npcure, or all
cure_link: Link function for the cure probability model
survival_dist: Distribution for modeling survival of uncured patients
bootstrap_ci: Use bootstrap for confidence interval estimation
n_bootstrap: Number of bootstrap samples
cure_threshold: Time threshold for defining cured patients
plot_cure_fraction: Generate cure fraction plot
plot_survival: Generate survival curve plots
goodness_of_fit: Conduct goodness of fit assessment
sensitivity_analysis: Conduct sensitivity analysis
use_background_mortality: Enable background mortality integration using cuRe package
background_hazard_var: Background hazard variable for cuRe models
npcure_covariate: Continuous covariate for npcure model (limited to one variable)
npcure_bandwidth: Bandwidth parameter for npcure kernel smoothing
npcure_time_points: Number of time points for testimate in npcure

Value

A results object containing:

`results$todo`					a html
`results$warnings`					a html
`results$errors`					a html
`results$summary`					a html
`results$modelTable`					a table
`results$cureTable`					a table
`results$cureFractionPlot`					an image
`results$survivalPlot`					an image
`results$goodnessOfFit`					a table
`results$sensitivityAnalysis`					a html
`results$modelComparison`					a table
`results$interpretation`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$modelTable$asDF

as.data.frame(results$modelTable)

Examples

# \donttest{
# Example usage
curemodels(
    data = cancer_data,
    time = followup_time,
    status = death_status,
    predictors = c(age, stage, treatment),
    model_type = "mixture"
)
#> Error: Argument 'time' contains 'followup_time' which is not present in the dataset
# }