Bootstrap assessment of the stability of multivariable models
Patrick Royston
Hub for Trials Methodology Research
MRC Clinical Trials Unit and University College London
London, UK
[email protected]
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Willi Sauerbrei
Institute for Medical Biometry and Medical Informatics
Freiburg University Medical Center
Freiburg, Germany
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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.
View all articles by these authors:
Patrick Royston, Willi Sauerbrei
View all articles with these keywords:
mfp, fracpoly, mfpboot, mfpboot_bif, pmbeval, pmbevalfn, pmbstabil, continuous covariates, fractional polynomials, multivariable modeling, stability, bootstrap, bagging
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