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62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

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Accuracy estimation after model selection using bootstrapping: An application to clinical data

Meeting Abstract

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  • Jakob Schöpe - Institute for Medical Biometry, Epidemiology and Medical Informatics, Saarland University, Campus Homburg, Homburg, Deutschland
  • Abdelshafi Bekhit - Institute for Medical Biometry, Epidemiology and Medical Informatics, Saarland University, Campus Homburg, Homburg, Deutschland
  • Gudrun Wagenpfeil - Institute for Medical Biometry, Epidemiology and Medical Informatics, Saarland University, Campus Homburg, Homburg, Deutschland
  • Antonius Schneider - Institute of General Practice, University Medical Center Klinikum rechts der Isar, TUM, München, Deutschland
  • Stefan Wagenpfeil - Institute for Medical Biometry, Epidemiology and Medical Informatics, Saarland University, Campus Homburg, Homburg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 215

doi: 10.3205/17gmds047, urn:nbn:de:0183-17gmds0473

Published: August 29, 2017

© 2017 Schöpe et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Predictive modeling in conjunction with model selection are commonly used in clinical research. However, uncertainty arising from model selection is not incorporated in classical theory of statistical inference. Accounting for model selection in statistical inference, Efron [1] recently developed a formula to approximate standard errors of smoothed estimators derived from bagging [2]. Therefore, the primary aim of this study was to implement and evaluate Efron's approach in R with an application to clinical data.

Clinical data was obtained from a previously published study [3], which was designed to develop clinical prediction rules for diagnosing asthma in patients suspected of suffering from obstructive respiratory disease. Smoothed estimators from non-parametric bootstrap replicates of stepwise fitted binomial logistic regression models and their approximated standard errors using Efron's approach were compared with results obtained from the delta method. Additionally, practical properties in extreme case problems were assessed using results from simulations. Findings and possible implications for penalized regression, especially L1-norm regularization, will be discussed.



Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass kein Ethikvotum erforderlich ist.

Outline

References

1.
Efron B. Estimation and accuracy after model selection. Journal of the American Statistical Association. 2014;109:991-1007.
2.
Breiman L. Bagging predictors. Machine Learning. 1996;24:123-140.
3.
Schneider A, Wagenpfeil G, Jörres RA, Wagenpfeil S. Influence of the practice setting on diagnostic prediction rules using FENO measurement in combination with clinical signs and symptoms of asthma. BMJ Open. 2015;5:e009676. DOI: 10.1136/bmjopen-2015-009676 External link