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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Generalized parametric cure models for relative survival.

Lasse Hjort Jakobsen1,2, Martin Bøgsted1,2, Mark Clements3

  • 1Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.

Biometrical Journal. Biometrische Zeitschrift
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

Cure models in survival analysis help estimate the cured proportion and survival of uncured individuals when events plateau. New latent cure models offer stable estimates, though potentially more biased than mixture cure models.

Keywords:
cure modelsparametric modelsrelative survivalsplines

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Cure models address scenarios where some individuals never experience an event or reach general population survival levels.
  • These models are crucial for understanding long-term outcomes in diseases like cancer, characterized by survival plateaus.
  • Existing cure models lack a unified formulation, necessitating new approaches.

Purpose of the Study:

  • To introduce a general parametric formulation for mixture cure models.
  • To propose a novel class of latent cure (LC) models.
  • To develop a comprehensive estimation framework and software for fitting diverse cure models.

Main Methods:

  • Developed a general parametric formulation for mixture cure models.
  • Introduced a new class of latent cure (LC) models.
  • Created a general estimation framework and accompanying software for model fitting.
  • Conducted simulations to evaluate model performance regarding cure proportion and uncured survival.
  • Applied models to colon cancer survival data exhibiting relative survival plateaus.

Main Results:

  • Mixture cure models, when not restricted to finite time plateaus, provide accurate cure proportion and uncured survival estimates.
  • Identifiability issues can render certain mixture cure models unstable.
  • Latent cure (LC) models generally yield stable estimates but may introduce bias.
  • Simulations confirmed the properties of both model types.

Conclusions:

  • The proposed framework and latent cure models offer a flexible approach to cure modeling in survival analysis.
  • Mixture and latent cure models have distinct advantages and disadvantages concerning accuracy and stability.
  • The choice of model depends on specific data characteristics and research objectives, particularly concerning potential identifiability issues and desired stability.