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Bootstrap assessment of the stability of multivariable models

Author

Listed:
  • Patrick Royston

    (MRC Clinical Trials Unit)

  • Willi Sauerbrei

    (Institute for Medical Biometry and Medical Informatics, Freiburg University Medical Center)

Abstract

Assessing the instability of a multivariable model is important but is rarely done in practice. Model instability occurs when selected predictors--and for multivariable fractional polynomial modeling, selected functions of continuous predictors--are sensitive to small changes in the data. Bootstrap analysis is a useful technique for investigating variations among selected models in samples drawn at random with replacement. Such samples mimic datasets that are structurally similar to that under study and that could plausibly have arisen instead. The bootstrap inclusion fraction of a candidate variable usefully indicates the importance of the variable. We describe Stata tools for stability analysis in the context of the mfp command for multivariable model building. We offer practical guidance and illustrate the application of the tools to a study in prostate cancer. Copyright 2009 by StataCorp LP.

Suggested Citation

  • Patrick Royston & Willi Sauerbrei, 2009. "Bootstrap assessment of the stability of multivariable models," Stata Journal, StataCorp LP, vol. 9(4), pages 547-570, December.
    • Handle: RePEc:tsj:stataj:v:9:y:2009:i:4:p:547-570
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    References listed on IDEAS

    as
    1. Willi Sauerbrei, 1999. "The Use of Resampling Methods to Simplify Regression Models in Medical Statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 313-329.
    2. Harald Binder & Willi Sauerbrei, 2009. "Stability analysis of an additive spline model for respiratory health data by using knot removal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 577-600, December.
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    Cited by:

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    2. Liu, Zicheng & Lesselier, Dominique & Sudret, Bruno & Wiart, Joe, 2020. "Surrogate modeling based on resampled polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Patrick Royston, 2012. "Tools to simulate realistic censored survival-time distributions," Stata Journal, StataCorp LP, vol. 12(4), pages 639-654, December.
    4. Dvouletý, Ondřej, 2018. "How to analyse determinants of entrepreneurship and self-employment at the country level? A methodological contribution," Journal of Business Venturing Insights, Elsevier, vol. 9(C), pages 92-99.

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