Estimation of the ROC curve and the area under it with complex survey data

Logistic regression models are widely applied in daily practice. Hence, it is necessary to ensure they have an adequate predictive performance, which is usually estimated by means of the receiver operating characteristic (ROC) curve and the area under it (area under the curve [AUC]). Traditional est...

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Detalhes bibliográficos
Autores: Iparragirre Letamendia, Amaia, Barrio Beraza, Irantzu, Arostegui Madariaga, Inmaculada
Tipo de documento: artigo
Data de publicação:2023
País:España
Recursos:Universidad del País Vasco
Repositório:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/72874
Acesso em linha:http://hdl.handle.net/10810/72874
Access Level:Acceso aberto
Palavra-chave:area under the curve
complex survey data
Mann–Whitney U-statistic
receiver operating characteristic curve
sampling weights
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spelling Estimation of the ROC curve and the area under it with complex survey dataIparragirre Letamendia, AmaiaBarrio Beraza, IrantzuArostegui Madariaga, Inmaculadaarea under the curvecomplex survey dataMann–Whitney U-statisticreceiver operating characteristic curvesampling weightsLogistic regression models are widely applied in daily practice. Hence, it is necessary to ensure they have an adequate predictive performance, which is usually estimated by means of the receiver operating characteristic (ROC) curve and the area under it (area under the curve [AUC]). Traditional estimators of these parameters are thought to be applied to simple random samples but are not appropriate for complex survey data. The goal of this work is to propose new weighted estimators for the ROC curve and AUC based on sampling weights which, in the context of complex survey data, indicate the number of units that each sampled observation represents in the popula- tion. The behaviour of the proposed estimators is evaluated and compared with the traditional unweighted ones by means of a simulation study. Finally, weighted and unweighted ROC curve and AUC estimators are applied to real survey data in order to compare the estimates in a real scenario. The results suggest the use of the weighted estimators proposed in this work in order to obtain unbiassed estimates for the ROC curve and AUC of logistic regression models fitted to complex survey data.Agencia Estatal de Investigación, Grant/Award Number: PID2020-115882RB-I00; Ministerio de Ciencia e Innovación, Grant/Award Number: CEX2021-001142-S; Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco, Grant/Award Number: IT1456-22; Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS); University of Basque Country.Wiley202520252023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/72874reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/info:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00/https://onlinelibrary.wiley.com/doi/10.1002/sta4.635info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/© 2023 The Authors. Stat published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.oai:addi.ehu.eus:10810/728742026-06-18T09:23:17Z
dc.title.none.fl_str_mv Estimation of the ROC curve and the area under it with complex survey data
title Estimation of the ROC curve and the area under it with complex survey data
spellingShingle Estimation of the ROC curve and the area under it with complex survey data
Iparragirre Letamendia, Amaia
area under the curve
complex survey data
Mann–Whitney U-statistic
receiver operating characteristic curve
sampling weights
title_short Estimation of the ROC curve and the area under it with complex survey data
title_full Estimation of the ROC curve and the area under it with complex survey data
title_fullStr Estimation of the ROC curve and the area under it with complex survey data
title_full_unstemmed Estimation of the ROC curve and the area under it with complex survey data
title_sort Estimation of the ROC curve and the area under it with complex survey data
dc.creator.none.fl_str_mv Iparragirre Letamendia, Amaia
Barrio Beraza, Irantzu
Arostegui Madariaga, Inmaculada
author Iparragirre Letamendia, Amaia
author_facet Iparragirre Letamendia, Amaia
Barrio Beraza, Irantzu
Arostegui Madariaga, Inmaculada
author_role author
author2 Barrio Beraza, Irantzu
Arostegui Madariaga, Inmaculada
author2_role author
author
dc.subject.none.fl_str_mv area under the curve
complex survey data
Mann–Whitney U-statistic
receiver operating characteristic curve
sampling weights
topic area under the curve
complex survey data
Mann–Whitney U-statistic
receiver operating characteristic curve
sampling weights
description Logistic regression models are widely applied in daily practice. Hence, it is necessary to ensure they have an adequate predictive performance, which is usually estimated by means of the receiver operating characteristic (ROC) curve and the area under it (area under the curve [AUC]). Traditional estimators of these parameters are thought to be applied to simple random samples but are not appropriate for complex survey data. The goal of this work is to propose new weighted estimators for the ROC curve and AUC based on sampling weights which, in the context of complex survey data, indicate the number of units that each sampled observation represents in the popula- tion. The behaviour of the proposed estimators is evaluated and compared with the traditional unweighted ones by means of a simulation study. Finally, weighted and unweighted ROC curve and AUC estimators are applied to real survey data in order to compare the estimates in a real scenario. The results suggest the use of the weighted estimators proposed in this work in order to obtain unbiassed estimates for the ROC curve and AUC of logistic regression models fitted to complex survey data.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/72874
url http://hdl.handle.net/10810/72874
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/
info:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00/
https://onlinelibrary.wiley.com/doi/10.1002/sta4.635
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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