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Fitting parametric cure models in R using the packages cuRe and rstpm2.

Rasmus Kuhr Jensen1, Mark Clements2, Lars Klingen Gjærde3

  • 1Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg 9000, Denmark.

Computer Methods and Programs in Biomedicine
|September 20, 2022
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Summary

Two R packages, cuRe and rstpm2, offer tools for parametric cure models, aiding analysis of time-to-event data and relative survival in medical research.

Keywords:
CuReCure modelsParametric modelsRelative survivalRstpm2Splines

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Research

Background:

  • Cure models are essential for analyzing time-to-event data when a proportion of subjects are expected to be cured or exhibit general population mortality rates.
  • These models are crucial for estimating the proportion of cured individuals and the event-time distribution for the uncured population.
  • Parametric cure models offer a robust framework for relative survival analysis in medical research.

Purpose of the Study:

  • To introduce two R packages, cuRe and rstpm2, designed for statistical inference using parametric cure models.
  • To provide researchers with flexible tools for fitting various parametric cure models, including spline-based formulations.
  • To facilitate the application of cure models for analyzing time-to-event data and relative survival.

Main Methods:

  • Implementation of parametric mixture cure models within the cuRe package, supporting both standard parametric distributions (Weibull, exponential) and spline-based approaches.
  • Development of the rstpm2 package for estimating spline-based latent cure models, which do not explicitly parameterize the cured proportion.
  • Utilizing a parametric framework to enable straightforward application of cure models for relative survival.

Main Results:

  • The cuRe package provides functions for fitting parametric mixture cure models using simple distributions or splines.
  • The rstpm2 package enables the estimation of latent cure models with flexible spline-based structures.
  • Both packages offer parametric solutions for cure modeling, enhancing analytical capabilities.

Conclusions:

  • The cuRe and rstpm2 R packages offer a comprehensive suite for fitting diverse parametric cure models.
  • cuRe includes post-estimation functions for calculating time to statistical cure and conditional cure probability.
  • These packages are expected to promote wider adoption of cure models in medical research for improved survival analysis.