Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments

Descripción del Articulo

Objectives: Traditional stated-preference models with fixed effects assume that individuals behave similarly. However, empirical evidence has shown that individuals’ preferences are often diverse. Hierarchical Bayesian models that include random effects provide individual-specific utilities to accou...

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Detalles Bibliográficos
Autores: Shi, A., Talledo Flores, Oscar Hernán
Formato: artículo
Fecha de Publicación:2017
Institución:Universidad San Ignacio de Loyola
Repositorio:USIL-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.usil.edu.pe:20.500.14005/3966
Enlace del recurso:https://hdl.handle.net/20.500.14005/3966
https://doi.org/10.1016/j.jval.2017.08.2122
Nivel de acceso:acceso embargado
Materia:Oncología Médica
Cáncer
Statistical inference
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spelling 9209dd9cc0c-cc52-42f7-8052-f3880fd98e55-1f0453524-6290-4c40-853a-63d1d76642fb-1Shi, A.Talledo Flores, Oscar Hernán2018-11-21T22:31:04Z2018-11-21T22:31:04Z2017-11Objectives: Traditional stated-preference models with fixed effects assume that individuals behave similarly. However, empirical evidence has shown that individuals’ preferences are often diverse. Hierarchical Bayesian models that include random effects provide individual-specific utilities to account for heterogeneity. This research studies oncologists’ choices about various pharmaceutical therapies for patients who have metastatic breast cancer. Methods: In this discrete choice experiment conducted in Lima, Peru, each of 113 oncologists was presented with 11 choice tasks (each consisting of four scenarios of therapies plus the NONE option) and asked to pick the best choice. The attributes included Treatment Scheme, Patient Recovery Status, Treatment Length, Cost, and Risk Factors. Hierarchical Bayesian methods were used in this multinomial logit conjoint analysis to account for heterogeneity in preferences.Revisado por paresapplication/pdfShi, A., & Talledo Flores, O. H. (2017). Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments. Value in Health, 20(9).10.1016/j.jval.2017.08.21221098-30151524-4733Value in Healthhttps://hdl.handle.net/20.500.14005/3966https://doi.org/10.1016/j.jval.2017.08.2122000413599902655engElsevier Science Inc.Value in Healthinfo:eu-repo/semantics/embargoedAccessUniversidad San Ignacio de LoyolaRepositorio Institucional - USILreponame:USIL-Institucionalinstname:Universidad San Ignacio de Loyolainstacron:USILOncología MédicaCáncerStatistical inferenceHierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatmentsinfo:eu-repo/semantics/articlePublication20.500.14005/3966oai:repositorio.usil.edu.pe:20.500.14005/39662023-04-17 11:01:05.251https://repositorio.usil.edu.peRepositorio institucional de la Universidad San Ignacio de Loyolarepositorio.institucional@usil.edu.pe
dc.title.es_ES.fl_str_mv Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
title Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
spellingShingle Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
Shi, A.
Oncología Médica
Cáncer
Statistical inference
title_short Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
title_full Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
title_fullStr Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
title_full_unstemmed Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
title_sort Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
author Shi, A.
author_facet Shi, A.
Talledo Flores, Oscar Hernán
author_role author
author2 Talledo Flores, Oscar Hernán
author2_role author
dc.contributor.author.fl_str_mv Shi, A.
Talledo Flores, Oscar Hernán
dc.subject.es_ES.fl_str_mv Oncología Médica
Cáncer
topic Oncología Médica
Cáncer
Statistical inference
dc.subject.en.fl_str_mv Statistical inference
description Objectives: Traditional stated-preference models with fixed effects assume that individuals behave similarly. However, empirical evidence has shown that individuals’ preferences are often diverse. Hierarchical Bayesian models that include random effects provide individual-specific utilities to account for heterogeneity. This research studies oncologists’ choices about various pharmaceutical therapies for patients who have metastatic breast cancer. Methods: In this discrete choice experiment conducted in Lima, Peru, each of 113 oncologists was presented with 11 choice tasks (each consisting of four scenarios of therapies plus the NONE option) and asked to pick the best choice. The attributes included Treatment Scheme, Patient Recovery Status, Treatment Length, Cost, and Risk Factors. Hierarchical Bayesian methods were used in this multinomial logit conjoint analysis to account for heterogeneity in preferences.
publishDate 2017
dc.date.accessioned.none.fl_str_mv 2018-11-21T22:31:04Z
dc.date.available.none.fl_str_mv 2018-11-21T22:31:04Z
dc.date.issued.fl_str_mv 2017-11
dc.type.es_ES.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.en.fl_str_mv Shi, A., & Talledo Flores, O. H. (2017). Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments. Value in Health, 20(9).
dc.identifier.doi.none.fl_str_mv 10.1016/j.jval.2017.08.2122
dc.identifier.issn.none.fl_str_mv 1098-3015
1524-4733
dc.identifier.journal.es_ES.fl_str_mv Value in Health
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.14005/3966
https://doi.org/10.1016/j.jval.2017.08.2122
dc.identifier.wos.none.fl_str_mv 000413599902655
identifier_str_mv Shi, A., & Talledo Flores, O. H. (2017). Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments. Value in Health, 20(9).
10.1016/j.jval.2017.08.2122
1098-3015
1524-4733
Value in Health
000413599902655
url https://hdl.handle.net/20.500.14005/3966
https://doi.org/10.1016/j.jval.2017.08.2122
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.en.fl_str_mv Value in Health
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.es_ES.fl_str_mv application/pdf
dc.publisher.en.fl_str_mv Elsevier Science Inc.
dc.source.es_ES.fl_str_mv Universidad San Ignacio de Loyola
Repositorio Institucional - USIL
dc.source.none.fl_str_mv reponame:USIL-Institucional
instname:Universidad San Ignacio de Loyola
instacron:USIL
instname_str Universidad San Ignacio de Loyola
instacron_str USIL
institution USIL
reponame_str USIL-Institucional
collection USIL-Institucional
repository.name.fl_str_mv Repositorio institucional de la Universidad San Ignacio de Loyola
repository.mail.fl_str_mv repositorio.institucional@usil.edu.pe
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score 13.140231
Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments
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