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flexsurv: A Platform for Parametric Survival Modeling in R.

Christopher H Jackson1

  • 1Christopher Jackson, MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, United Kingdom.

Journal of Statistical Software
|March 30, 2018
PubMed
Summary

The flexsurv R package offers flexible parametric modeling for time-to-event survival data. It allows fitting various distributions and complex covariate effects, enhancing survival analysis capabilities.

Keywords:
multi-state modelsmultistate modelssurvival

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Survival Analysis

Background:

  • Parametric modeling of survival data is crucial for understanding time-to-event phenomena in various scientific fields.
  • Existing R packages may have limitations in flexibility for modeling complex survival distributions and covariate interactions.
  • The need for a versatile tool to handle diverse parametric survival models and multi-state processes is evident.

Purpose of the Study:

  • Introduce the flexsurv R package for advanced parametric survival data analysis.
  • Demonstrate the package's capability to fit arbitrary parametric distributions and flexible covariate effects.
  • Showcase flexsurv's functionality for multi-state models and integration with the mstate package.

Main Methods:

  • Utilizes fully-parametric modeling for time-to-event data, allowing user-defined probability density or hazard functions.
  • Incorporates standard survival distributions (e.g., generalized gamma, F distributions) and a flexible spline model.
  • Employs maximum log-likelihood estimation, compatible with the survival package's syntax for handling censoring and left-truncation.

Main Results:

  • flexsurv enables fitting a wide range of parametric survival models with flexible covariate effects.
  • The package supports the estimation and plotting of model parameters and confidence intervals.
  • Demonstrates successful application in fitting fully-parametric multi-state models.

Conclusions:

  • flexsurv provides a powerful and flexible R package for advanced parametric survival analysis.
  • The package enhances the ability to model complex time-to-event data and multi-state processes.
  • Its design facilitates ease of use for researchers familiar with standard survival analysis packages.