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Assumptions of Survival Analysis01:15

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function.

Benjamin Kearns1, Matt D Stevenson1, Kostas Triantafyllopoulos1

  • 1The University of Sheffield, Sheffield, UK.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|September 27, 2019
PubMed
Summary
This summary is machine-generated.

Generalized linear models (GLMs) offer more flexible parametric survival analysis than standard models. While GLMs improved within-sample fit and extrapolation plausibility in a case study, they did not enhance extrapolation performance.

Keywords:
dynamic survival modelsfractional polynomialsfrailty modelsgeneralized additive modelsgeneralized linear mixed modelssplinessurvival analysistime to event

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Parametric survival modeling is crucial for reimbursement decisions.
  • Accurate predictions necessitate flexible models for hazard function evolution.
  • Generalized linear models (GLMs) offer flexibility but are underutilized in survival analysis.

Purpose of the Study:

  • To explore theoretical properties of GLMs for survival data.
  • To compare GLM performance against standard survival models.
  • To provide guidance on implementing and selecting GLM-based survival models.

Main Methods:

  • Analysis of survival data using GLMs and extensions (fractional polynomials, splines, GAMs, frailty models, dynamic models).
  • Comparison of model strengths and limitations.
  • Case study evaluating within-sample fit, extrapolation plausibility, and performance using data splitting.

Main Results:

  • Standard survival models often assume restrictive linearity when viewed as GLMs.
  • GLMs demonstrated superior within-sample fit and more plausible extrapolations in the case study.
  • Extrapolation performance was not improved by GLMs, and improvements in fit were modest.

Conclusions:

  • GLMs offer a viable, flexible alternative to standard parametric survival models.
  • GLM-based approaches are currently underused in survival analysis.
  • Guidance and a reproducible case study are provided to encourage adoption of GLMs.