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Related Experiment Videos

A bootstrap resampling procedure for model building: application to the Cox regression model.

W Sauerbrei1, M Schumacher

  • 1Institute of Medical Biometry and Informatics, University of Freiburg, Germany.

Statistics in Medicine
|December 1, 1992
PubMed
Summary
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This study introduces a bootstrap-model selection procedure to improve variable selection in regression models for clinical trials. It enhances model stability and reliability for analyzing cancer data using Cox regression.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Variable selection in regression models is crucial for clinical study analysis.
  • Stepwise methods are common but face criticism regarding model stability.
  • Systematic stability investigations are often lacking.

Purpose of the Study:

  • To develop a data-dependent bootstrap-model selection procedure.
  • To enhance the stability and reliability of variable selection in regression models.
  • To address challenges in clinical trial data analysis, including covariate adjustment and interaction analysis.

Main Methods:

  • Extending the bootstrap resampling procedure proposed by Chen and George.
  • Combining bootstrap resampling with established selection techniques like stepwise methods.

Related Experiment Videos

  • Applying the procedure to Cox proportional hazard regression models.

    • Main Results:

    • The proposed bootstrap-model selection strategy was illustrated using data from two cancer clinical trials.
    • Demonstrated application in selecting 'mild' effects for covariate adjustment (brain tumor study).
    • Showcased application in selecting 'strong' effects for treatment-covariate interactions (prostate cancer study).

    Conclusions:

    • The bootstrap-model selection procedure offers a robust alternative to traditional methods for variable selection.
    • The strategy is adaptable for different clinical research scenarios and regression models.
    • Enhances the systematic investigation of model stability in clinical research.