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Titolo:
Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis
Autore:
Steyerberg, EW; Harrell, FE; Borsboom, GJJM; Eijkemans, MJC; Vergouwe, Y; Habbema, JDF;
Indirizzi:
Erasmus Univ, Dept Publ Hlth, Ctr Clin Decis Sci, NL-3000 DR Rotterdam, Netherlands Erasmus Univ Rotterdam Netherlands NL-3000 DR DR Rotterdam, Netherlands Univ Virginia, Dept Hlth Evaluat Sci, Div Biostat & Epidemiol, Charlottesville, VA USA Univ Virginia Charlottesville VA USA Epidemiol, Charlottesville, VA USA
Titolo Testata:
JOURNAL OF CLINICAL EPIDEMIOLOGY
fascicolo: 8, volume: 54, anno: 2001,
pagine: 774 - 781
SICI:
0895-4356(200108)54:8<774:IVOPME>2.0.ZU;2-Q
Fonte:
ISI
Lingua:
ENG
Soggetto:
ACUTE MYOCARDIAL-INFARCTION; INDIVIDUAL PATIENT DATA; SMALL DATA SETS; PROGNOSTIC MODELS; CROSS-VALIDATION; SELECTION; PROBABILITY; SIMULATION; INFERENCE; DIAGNOSIS;
Keywords:
predictive models; internal validation; logistic regression analysis; bootstrapping;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
33
Recensione:
Indirizzi per estratti:
Indirizzo: Steyerberg, EW Erasmus Univ, Dept Publ Hlth, Ctr Clin Decis Sci, Ee 2091,POB 1738, NL-3000 DR Rotterdam, Netherlands Erasmus Univ Ee 2091,POB 1738 Rotterdam Netherlands NL-3000 DR
Citazione:
E.W. Steyerberg et al., "Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis", J CLIN EPID, 54(8), 2001, pp. 774-781

Abstract

The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Severalinternal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortalityafter an acute myocardial infarction. Random samples with a size between,n= 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance with large variability. Cross-validation on 10%of the sample had low bias and low variability, but was not suitable fur all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude thatsplit-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model. (C) 2001 Elsevier Science Inc. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 27/11/20 alle ore 22:18:29