Bootstrap assessment of the stability of multivariable models

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.


Issue Date:
2009
Publication Type:
Journal Article
DOI and Other Identifiers:
st0177 (Other)
PURL Identifier:
http://purl.umn.edu/143012
Published in:
Stata Journal, Volume 09, Number 4
Page range:
547-570
Total Pages:
24

Record appears in:



 Record created 2017-04-01, last modified 2017-08-26

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)