Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation,... | Biowebspin
Journal of clinical epidemiology. 2012 02 24

Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming.

?



Citimpact
Janssen KJ, Siccama I, Vergouwe Y, Koffijberg H, Debray TP, Keijzer M, Grobbee DE, Moons KG. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Abstract
OBJECTIVE: Many prediction models are developed by multivariable logistic regression. However, there are several alternative methods to develop prediction models. We compared the accuracy of a model that predicts the presence of deep venous thrombosis (DVT) when developed by four different methods.

STUDY DESIGN AND SETTING: We used the data of 2,086 primary care patients suspected of DVT, which included 21 candidate predictors. The cohort was split into a derivation set (1,668 patients, 329 with DVT) and a validation set (418 patients, 86 with DVT). Also, 100 cross-validations were conducted in the full cohort. The models were developed by logistic regression, logistic regression with shrinkage by bootstrapping techniques, logistic regression with shrinkage by penalized maximum likelihood estimation, and genetic programming. The accuracy of the models was tested by assessing discrimination and calibration.

RESULTS: There were only marginal differences in the discrimination and calibration of the models in the validation set and cross-validations.

CONCLUSION: The accuracy measures of the models developed by the four different methods were only slightly different, and the 95% confidence intervals were mostly overlapped. We have shown that models with good predictive accuracy are most likely developed by sensible modeling strategies rather than by complex development methods. Copyright © 2012 Elsevier Inc. All rights reserved.

PMID: 22214734

LinkOut - more resources

Full Text SourcesElsevier Science
ClinicalKey
Other Literature SourcesFaculty of 1000
MedicalMedlinePlus Health Information
Title
Abstract
Authors
Journal
ISSN
Volume
Issue
Chapter
Edition
Start page
End page
Year
Month
Day
Book Title
Institution
Share on social networks :
Look on Google if the pdf is available or the publication is cited in other pdfs