Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy

Surrogate endpoints have been used to assess the efficacy of a treatment and can potentially reduce the duration and/or number of required patients for clinical trials. Using information theory, Alonso et al. (2007) proposed a unified framework based on Shannon entropy, a new definition of surrogacy...

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Detalhes bibliográficos
Autores: Pardo Llorente, María del Carmen, Zhao, Qian, Jin, Hua, Lu, Ying
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/73049
Acesso em linha:https://hdl.handle.net/20.500.14352/73049
Access Level:acceso abierto
Palavra-chave:519.22
Surrogate endpoint
Information theory
Havrda and Charvat entropy
Mutual information
Clinical trial design
Estadística matemática (Matemáticas)
1209 Estadística
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oai_identifier_str oai:docta.ucm.es:20.500.14352/73049
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spelling Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropyPardo Llorente, María del CarmenZhao, QianJin, HuaLu, Ying519.22Surrogate endpointInformation theoryHavrda and Charvat entropyMutual informationClinical trial designEstadística matemática (Matemáticas)1209 EstadísticaSurrogate endpoints have been used to assess the efficacy of a treatment and can potentially reduce the duration and/or number of required patients for clinical trials. Using information theory, Alonso et al. (2007) proposed a unified framework based on Shannon entropy, a new definition of surrogacy that departed from the hypothesis testing framework. In this paper, a new family of surrogacy measures under Havrda and Charvat (H-C) entropy is derived which contains Alonso’s definition as a particular case. Furthermore, we extend our approach to a new model based on the information-theoretic measure of association for a longitudinally collected continuous surrogate endpoint for a binary clinical endpoint of a clinical trial using H-C entropy. The new model is illustrated through the analysis of data from a completed clinical trial. It demonstrates advantages of H-C entropy-based surrogacy measures in the evaluation of scheduling longitudinal biomarker visits for a phase 2 randomized controlled clinical trial for treatment of multiple sclerosis.MDPIUniversidad Complutense de Madrid20222022-01-3120222022-01-31journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/73049reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/730492026-06-02T12:44:21Z
dc.title.none.fl_str_mv Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
title Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
spellingShingle Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
Pardo Llorente, María del Carmen
519.22
Surrogate endpoint
Information theory
Havrda and Charvat entropy
Mutual information
Clinical trial design
Estadística matemática (Matemáticas)
1209 Estadística
title_short Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
title_full Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
title_fullStr Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
title_full_unstemmed Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
title_sort Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
dc.creator.none.fl_str_mv Pardo Llorente, María del Carmen
Zhao, Qian
Jin, Hua
Lu, Ying
author Pardo Llorente, María del Carmen
author_facet Pardo Llorente, María del Carmen
Zhao, Qian
Jin, Hua
Lu, Ying
author_role author
author2 Zhao, Qian
Jin, Hua
Lu, Ying
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.22
Surrogate endpoint
Information theory
Havrda and Charvat entropy
Mutual information
Clinical trial design
Estadística matemática (Matemáticas)
1209 Estadística
topic 519.22
Surrogate endpoint
Information theory
Havrda and Charvat entropy
Mutual information
Clinical trial design
Estadística matemática (Matemáticas)
1209 Estadística
description Surrogate endpoints have been used to assess the efficacy of a treatment and can potentially reduce the duration and/or number of required patients for clinical trials. Using information theory, Alonso et al. (2007) proposed a unified framework based on Shannon entropy, a new definition of surrogacy that departed from the hypothesis testing framework. In this paper, a new family of surrogacy measures under Havrda and Charvat (H-C) entropy is derived which contains Alonso’s definition as a particular case. Furthermore, we extend our approach to a new model based on the information-theoretic measure of association for a longitudinally collected continuous surrogate endpoint for a binary clinical endpoint of a clinical trial using H-C entropy. The new model is illustrated through the analysis of data from a completed clinical trial. It demonstrates advantages of H-C entropy-based surrogacy measures in the evaluation of scheduling longitudinal biomarker visits for a phase 2 randomized controlled clinical trial for treatment of multiple sclerosis.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-31
2022
2022-01-31
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/73049
url https://hdl.handle.net/20.500.14352/73049
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405325108445184
score 15.301603

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