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Goftte: A R package for assessing goodness-of-fit in proportional (sub) distributions hazards regression models.

P Sfumato1, T Filleron2, R Giorgi3

  • 1Institut Paoli-Calmettes, Biostatistics Unit, Marseille, France.

Computer Methods and Programs in Biomedicine
|July 20, 2019
PubMed
Summary

The new R package goftte offers goodness-of-fit tests for Cox and Fine-Gray models using cumulative sums of residuals. It provides reliable error rate control, aiding semiparametric regression analysis.

Keywords:
Competing risksCumulative sums of residualsGoftteGoodness-of-fitSurvival data

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Area of Science:

  • Statistics
  • Biostatistics
  • Computational Statistics

Background:

  • Assessing goodness-of-fit is crucial for validating statistical models.
  • Cox regression and Fine-Gray regression models are widely used for time-to-event data analysis.
  • Existing methods may lack comprehensive tools for assumption checking in these models.

Purpose of the Study:

  • Introduce the R package goftte for goodness-of-fit assessment.
  • Provide methods for checking key assumptions in Cox and Fine-Gray regression models.
  • Facilitate the application of advanced goodness-of-fit techniques.

Main Methods:

  • Utilize cumulative sums of model residuals for goodness-of-fit testing.
  • Employ Monte-Carlo methods to approximate null distributions.
  • Implement core routines in C++ for computational efficiency and parallel processing.

Main Results:

  • Simulation studies demonstrate excellent control of Type I error rates.
  • The package performs well even with small sample sizes and high censoring rates.
  • Anderson-Darling type test statistics are incorporated for proportional hazards assessment.

Conclusions:

  • goftte offers novel testing functionals compared to existing R packages.
  • Validated null distribution approximations through simulation experiments.
  • Future versions will extend capabilities to recurrent event data analysis.