Some properties of regression estimators in GEE models for clustered ordinal data

Autores
Nores, Maria Laura; Diaz, Maria del Pilar
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper we study properties of the estimators of marginal mean parameters in the GEE1approac h of Heagerty and Zeger (J. Amer. Statist. Assoc. 91 (1996) 1024) for clustered ordinal data. We consider two aspects: coverage probabilities and efficiency. The first point was tackled by a simulation study, calculating empirical levels of confidence intervals for regression parameters using different sample sizes. We conclude that inferences have more validity for sample sizes greater than 100, while some care must be taken when the number of clusters is smaller since in several situations empirical levels were much lower than nominal levels. Regarding the second aspect, we studied asymptotic efficiency for the case of an independence working specification in relation to a correctly specified exchangeable association structure. We extended to ordinal measurements the results derived for binary outcomes, sustaining that the loss of efficiency depends both on the intensity of the association between responses and the design matrix. We showed that relative efficiency of independence to exchangeable estimator is high when responses are independent, when covariates are mean-balanced, or when all covariates are constant within clusters. However, relative efficiency noticeably declines with increasing association for non mean-balanced within-cluster covariates. Simulation studies also supported these conclusions for data with an approximately exchangeable association structure.
Fil: Nores, Maria Laura. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Diaz, Maria del Pilar. Universidad Nacional de Córdoba. Facultad de Medicina. Escuela de Nutrición; Argentina
Materia
COVERAGE PROBABILITIES
EFFICIENCY
ASSOCIATION
GLOBAL ODDS RATIOS
COVARIATE DESIGN
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/242194
Acceder
id CONICETDig_43ae7bb15636bc222d7a2c4fe97d878a
oai_identifier_str oai:ri.conicet.gov.ar:11336/242194
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Some properties of regression estimators in GEE models for clustered ordinal dataNores, Maria LauraDiaz, Maria del PilarCOVERAGE PROBABILITIESEFFICIENCYASSOCIATIONGLOBAL ODDS RATIOSCOVARIATE DESIGNhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper we study properties of the estimators of marginal mean parameters in the GEE1approac h of Heagerty and Zeger (J. Amer. Statist. Assoc. 91 (1996) 1024) for clustered ordinal data. We consider two aspects: coverage probabilities and efficiency. The first point was tackled by a simulation study, calculating empirical levels of confidence intervals for regression parameters using different sample sizes. We conclude that inferences have more validity for sample sizes greater than 100, while some care must be taken when the number of clusters is smaller since in several situations empirical levels were much lower than nominal levels. Regarding the second aspect, we studied asymptotic efficiency for the case of an independence working specification in relation to a correctly specified exchangeable association structure. We extended to ordinal measurements the results derived for binary outcomes, sustaining that the loss of efficiency depends both on the intensity of the association between responses and the design matrix. We showed that relative efficiency of independence to exchangeable estimator is high when responses are independent, when covariates are mean-balanced, or when all covariates are constant within clusters. However, relative efficiency noticeably declines with increasing association for non mean-balanced within-cluster covariates. Simulation studies also supported these conclusions for data with an approximately exchangeable association structure.Fil: Nores, Maria Laura. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Diaz, Maria del Pilar. Universidad Nacional de Córdoba. Facultad de Medicina. Escuela de Nutrición; ArgentinaElsevier Science2008-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/242194Nores, Maria Laura; Diaz, Maria del Pilar; Some properties of regression estimators in GEE models for clustered ordinal data; Elsevier Science; Computational Statistics and Data Analysis; 52; 7; 3-2008; 3877-38880167-9473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947307004689info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2007.12.009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:15:10Zoai:ri.conicet.gov.ar:11336/242194instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:15:10.916CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Some properties of regression estimators in GEE models for clustered ordinal data
title Some properties of regression estimators in GEE models for clustered ordinal data
spellingShingle Some properties of regression estimators in GEE models for clustered ordinal data
Nores, Maria Laura
COVERAGE PROBABILITIES
EFFICIENCY
ASSOCIATION
GLOBAL ODDS RATIOS
COVARIATE DESIGN
title_short Some properties of regression estimators in GEE models for clustered ordinal data
title_full Some properties of regression estimators in GEE models for clustered ordinal data
title_fullStr Some properties of regression estimators in GEE models for clustered ordinal data
title_full_unstemmed Some properties of regression estimators in GEE models for clustered ordinal data
title_sort Some properties of regression estimators in GEE models for clustered ordinal data
dc.creator.none.fl_str_mv Nores, Maria Laura
Diaz, Maria del Pilar
author Nores, Maria Laura
author_facet Nores, Maria Laura
Diaz, Maria del Pilar
author_role author
author2 Diaz, Maria del Pilar
author2_role author
dc.subject.none.fl_str_mv COVERAGE PROBABILITIES
EFFICIENCY
ASSOCIATION
GLOBAL ODDS RATIOS
COVARIATE DESIGN
topic COVERAGE PROBABILITIES
EFFICIENCY
ASSOCIATION
GLOBAL ODDS RATIOS
COVARIATE DESIGN
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper we study properties of the estimators of marginal mean parameters in the GEE1approac h of Heagerty and Zeger (J. Amer. Statist. Assoc. 91 (1996) 1024) for clustered ordinal data. We consider two aspects: coverage probabilities and efficiency. The first point was tackled by a simulation study, calculating empirical levels of confidence intervals for regression parameters using different sample sizes. We conclude that inferences have more validity for sample sizes greater than 100, while some care must be taken when the number of clusters is smaller since in several situations empirical levels were much lower than nominal levels. Regarding the second aspect, we studied asymptotic efficiency for the case of an independence working specification in relation to a correctly specified exchangeable association structure. We extended to ordinal measurements the results derived for binary outcomes, sustaining that the loss of efficiency depends both on the intensity of the association between responses and the design matrix. We showed that relative efficiency of independence to exchangeable estimator is high when responses are independent, when covariates are mean-balanced, or when all covariates are constant within clusters. However, relative efficiency noticeably declines with increasing association for non mean-balanced within-cluster covariates. Simulation studies also supported these conclusions for data with an approximately exchangeable association structure.
Fil: Nores, Maria Laura. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Diaz, Maria del Pilar. Universidad Nacional de Córdoba. Facultad de Medicina. Escuela de Nutrición; Argentina
description In this paper we study properties of the estimators of marginal mean parameters in the GEE1approac h of Heagerty and Zeger (J. Amer. Statist. Assoc. 91 (1996) 1024) for clustered ordinal data. We consider two aspects: coverage probabilities and efficiency. The first point was tackled by a simulation study, calculating empirical levels of confidence intervals for regression parameters using different sample sizes. We conclude that inferences have more validity for sample sizes greater than 100, while some care must be taken when the number of clusters is smaller since in several situations empirical levels were much lower than nominal levels. Regarding the second aspect, we studied asymptotic efficiency for the case of an independence working specification in relation to a correctly specified exchangeable association structure. We extended to ordinal measurements the results derived for binary outcomes, sustaining that the loss of efficiency depends both on the intensity of the association between responses and the design matrix. We showed that relative efficiency of independence to exchangeable estimator is high when responses are independent, when covariates are mean-balanced, or when all covariates are constant within clusters. However, relative efficiency noticeably declines with increasing association for non mean-balanced within-cluster covariates. Simulation studies also supported these conclusions for data with an approximately exchangeable association structure.
publishDate 2008
dc.date.none.fl_str_mv 2008-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/242194
Nores, Maria Laura; Diaz, Maria del Pilar; Some properties of regression estimators in GEE models for clustered ordinal data; Elsevier Science; Computational Statistics and Data Analysis; 52; 7; 3-2008; 3877-3888
0167-9473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/242194
identifier_str_mv Nores, Maria Laura; Diaz, Maria del Pilar; Some properties of regression estimators in GEE models for clustered ordinal data; Elsevier Science; Computational Statistics and Data Analysis; 52; 7; 3-2008; 3877-3888
0167-9473
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947307004689
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2007.12.009
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781581905100800
score 12.982451

Publicaciones similares