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Semiparametric competing risks regression under interval censoring using the R package intccr.

Jun Park1, Giorgos Bakoyannis1, Constantin T Yiannoutsos1

  • 1Department of Biostatistics Richard M. Fairbanks School of Public Health Indiana University School of Medicine 410 W. 10th Street Suite 3000 Indianapolis, IN 46202, United States of America.

Computer Methods and Programs in Biomedicine
|May 4, 2019
PubMed
Summary

This study introduces the R package intccr for analyzing interval-censored competing risks data. The software implements semiparametric regression for cumulative incidence functions without restrictive assumptions.

Keywords:
Competing risksInterval censoringProportional hazards modelProportional odds modelSemiparametric regressionSurvival analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Software Development

Background:

  • Competing risk data often involves interval-censored event times, where exact event times are unknown.
  • Accurate analysis of interval-censored competing risks data is crucial in various applications but presents statistical challenges.
  • Existing methods may require restrictive parametric assumptions or lack user-friendly implementations.

Purpose of the Study:

  • To develop and present an easy-to-use, open-source statistical software for analyzing interval-censored competing risks data.
  • To provide a flexible tool for semiparametric regression analysis of the cumulative incidence function (CIF).
  • To implement advanced statistical methodologies without imposing strong parametric constraints.

Main Methods:

  • Utilized the sieve maximum likelihood method based on B-splines for semiparametric regression.
  • Developed the R package 'intccr' for practical implementation of the proposed methodology.
  • Ensured the methodology provides semiparametrically efficient estimates.

Main Results:

  • The 'intccr' R package enables semiparametric regression analysis of CIF with interval-censored competing risks data.
  • The package supports various models, including proportional odds and Fine-Gray proportional subdistribution hazards models.
  • It offers functionality for estimating CIFs for specific covariate values and managing long-format data.

Conclusions:

  • The 'intccr' R package offers a convenient and flexible software solution.
  • It facilitates the analysis of cumulative incidence functions for interval-censored competing risks data.
  • This tool enhances the practical application of advanced statistical methods in biostatistics.