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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Generalized survival models for correlated time-to-event data.

Xing-Rong Liu1, Yudi Pawitan1, Mark S Clements1

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, S-171 77 Stockholm, Sweden.

Statistics in Medicine
|September 15, 2017
PubMed
Summary

This study introduces a flexible framework for modeling correlated time-to-event data using generalized survival models with shared random effects. The approach effectively handles time-dependent and nonlinear effects, demonstrating good performance in simulations and applications.

Keywords:
adaptive Gauss-Hermite quadraturecorrelated survival datageneralized survival modelslink functionsrandom effectstensor product

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Modeling correlated time-to-event data is crucial in various fields.
  • Existing methods may lack flexibility in handling complex effects.
  • A unified framework for correlated survival data with rich postestimation is needed.

Purpose of the Study:

  • To develop a flexible framework for modeling correlated time-to-event data.
  • To extend generalized survival models with shared frailty or random effects.
  • To incorporate time-dependent, nonlinear effects, and interactions.

Main Methods:

  • Extended generalized survival models with shared frailty/random effects.
  • Incorporated parametric or penalized smooth functions for time-varying and nonlinear effects.
  • Used maximum (penalized) marginal likelihood estimation with automatic smoothing parameter selection via cross-validation.
  • Applied Gauss-Hermite quadrature for normal random effects and Akaike Information Criterion for model comparison.

Main Results:

  • The proposed framework performs well in simulations for both small and large clusters.
  • Demonstrated comparison of proportional hazards and proportional odds models for clustered survival data.
  • Successfully estimated time-varying effects on the log-time scale and age-varying effects.

Conclusions:

  • The developed framework provides a robust and flexible approach for analyzing correlated time-to-event data.
  • The `rstpm2` R package facilitates the implementation of these advanced statistical methods.
  • The approach allows for detailed investigation of complex time- and covariate-dependent effects.