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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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An extended cure model and model selection.

Yingwei Peng1, Jianfeng Xu

  • 1Department of Community Health and Epidemiology, Queen's University, Kingston, ON, K7L 3N6, Canada. pengp@queensu.ca

Lifetime Data Analysis
|January 14, 2012
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Summary
This summary is machine-generated.

This study extends the Box-Cox transformation cure model, offering methods to distinguish between mixture and bounded cumulative hazard models. Statistical tests and AIC effectively differentiate models with large sample sizes.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Cure models are essential for analyzing data with a proportion of individuals who will not experience the event of interest.
  • Existing Box-Cox transformation cure models require further interpretation and extension.
  • Distinguishing between different cure model structures is crucial for accurate survival analysis.

Purpose of the Study:

  • To propose a novel interpretation and extension of the Box-Cox transformation cure model.
  • To develop and evaluate methods for model selection between mixture cure and bounded cumulative hazard cure models.
  • To assess the performance of likelihood ratio tests, score tests, and Akaike's Information Criterion (AIC) for model discrimination.

Main Methods:

  • Extension of the Box-Cox transformation cure model.
  • Application of likelihood ratio tests and score tests for hypothesis testing.
  • Utilizing Akaike's Information Criterion (AIC) for model selection.
  • Empirical studies with simulated data and real-world cancer datasets (leukemia, colon cancer).

Main Results:

  • The extended Box-Cox transformation cure model provides a natural framework for cure rate analysis.
  • Akaike's Information Criterion (AIC) demonstrates informativeness in model selection.
  • Likelihood ratio tests and score tests exhibit adequate power to differentiate between mixture and bounded cumulative hazard models, particularly with large sample sizes.
  • The proposed methods were successfully applied to leukemia and colon cancer data.

Conclusions:

  • The extended Box-Cox cure model offers a valuable approach to survival data analysis.
  • Statistical tests and AIC are reliable tools for selecting appropriate cure models.
  • The findings support the use of these methods for analyzing cancer survival data and understanding cure proportions.