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frailtypack: a computer program for the analysis of correlated failure time data using penalized likelihood

Virginie Rondeau1, Juan R Gonzalez

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This study introduces frailtypack, an R package for analyzing correlated survival data. It provides smoothed estimates of the baseline hazard function using shared gamma frailty models.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Computational Statistics

Background:

  • Correlated survival outcomes are common in biomedical research.
  • Existing software has limitations for smoothed hazard function estimation in random effects models for correlated data.

Purpose of the Study:

  • To describe the R package `frailtypack` for survival data analysis.
  • To enable parameter estimation in shared gamma frailty models with complex data structures.
  • To provide smoothed estimates of the baseline hazard function.

Main Methods:

  • Utilizes penalized likelihood estimation for parameter estimation.
  • Accommodates right-censored, left-truncated, and stratified survival data.
  • Supports time-dependent covariates and recurrent events using the Andersen-Gill approach.

Main Results:

  • The `frailtypack` package effectively estimates parameters in shared gamma frailty models.
  • The package can directly compute smooth estimates of the baseline hazard function.
  • Demonstrated application on datasets with clustered survival times and recurrent events (colorectal cancer rehospitalizations).

Conclusions:

  • The `frailtypack` R package offers a valuable tool for advanced survival data analysis.
  • It addresses limitations in existing software for correlated survival data and hazard function estimation.
  • Facilitates the modeling of complex survival data, including recurrent events and time-dependent covariates.