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Assessment of survival prediction models based on microarray data.

Martin Schumacher1, Harald Binder, Thomas Gerds

  • 1Department of Medical Biometry and Statistics, Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. sec@imbi.uni-freiburg.de

Bioinformatics (Oxford, England)
|May 9, 2007
PubMed
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Overfitting in risk prediction models can lead to errors. This study uses resampling methods to accurately estimate prediction error and detect overfitting, ensuring reliable model performance assessment.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Machine Learning

Background:

  • Developing risk prediction models involves complex steps.
  • Inadequate control during model building can lead to overfitting and overoptimism.
  • Overfitting may result in erroneous conclusions in clinical practice.

Purpose of the Study:

  • To assess the performance of risk prediction models using prediction error estimation.
  • To detect overfitting and overoptimism in model development.
  • To adjust prediction error estimates using resampling methods.

Main Methods:

  • Estimating prediction error for right-censored time-to-event data.
  • Employing resampling methods to detect overfitting.
  • Applying the methodology to multivariate Cox regression models.

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Main Results:

  • Demonstrating the methodology's utility in high-dimensional settings prone to overfitting.
  • Illustrating the approach with a prognostic study in diffuse large-B-cell lymphoma (DLBCL).
  • Estimating prediction error for recently proposed Cox regression fitting techniques.

Conclusions:

  • The proposed methods effectively detect and adjust for overfitting in risk prediction models.
  • Accurate prediction error estimation is crucial for reliable model performance assessment.
  • The methodology is applicable to complex datasets, including those with many potential predictors.