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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/242194
Ver los metadatos del registro completo
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 Técnicas2025-09-29T09:46:20Zoai: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:34982025-09-29 09:46:21.196CONICET 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_ |
1844613447562559488 |
score |
13.070432 |