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Parametric and penalized generalized survival models.

Xing-Rong Liu1, Yudi Pawitan1, Mark Clements1

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.

Statistical Methods in Medical Research
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

Generalized survival models offer flexible analysis of time-to-event data. These models, including proportional hazards and odds, simplify estimation and perform well in simulations for cancer survival analysis.

Keywords:
Generalized survival modelslink functionspenalized likelihoodsmooth functionsurvival data

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Traditional survival models like proportional hazards have limitations.
  • Need for flexible models accommodating complex time-dependent effects.

Purpose of the Study:

  • Introduce generalized survival models (GSMs) for flexible time-to-event data analysis.
  • Extend existing parametric and semi-parametric survival models.
  • Provide robust estimation and model selection methods.

Main Methods:

  • Modeled g(S(t|z)) using linear predictors with smooth time and covariate effects.
  • Incorporated penalized smoothers and parametric effects for estimation.
  • Utilized information criteria for automatic selection of smoothing parameters and link functions.
  • Implemented models in R, leveraging the mgcv package.

Main Results:

  • GSMs demonstrated strong performance in simulation studies compared to existing models.
  • Simplified estimation of smooth covariate and time-dependent effects.
  • Proportional odds models outperformed proportional hazards models in two of three cancer survival datasets.

Conclusions:

  • Generalized survival models provide a powerful and flexible framework for survival data.
  • The models offer improved estimation and selection capabilities.
  • Findings suggest the proportional odds model may be preferable for certain cancer survival data structures.