Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances

Detalhes bibliográficos
Autor(a) principal: Ali, M. Sanni
Data de Publicação: 2019
Outros Autores: Alhambra, Daniel Prieto, Lopes, Luciane Cruz, Ramos, Dandara, Bispo, Nivea, Ichihara, Maria Y., Pescarini, Julia M., Williamson, Elizabeth, Fiaccone, Rosemeire L., Barreto, Mauricio Lima, Smeeth, Liam
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/39607
Resumo: The 100 Million Brazilian Cohort project funded by the Wellcome Trust. Grant code: 202912/B/16/Z.
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spelling Ali, M. SanniAlhambra, Daniel PrietoLopes, Luciane CruzRamos, DandaraBispo, NiveaIchihara, Maria Y.Pescarini, Julia M.Williamson, ElizabethFiaccone, Rosemeire L.Barreto, Mauricio LimaSmeeth, Liam2020-01-29T18:13:14Z2020-01-29T18:13:14Z2019ALI, M Sanni et al. Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances. Frontiers in Pharmacology, p. 1-19, 2019.1663-9812https://www.arca.fiocruz.br/handle/icict/3960710.3389/fphar.2019.00973The 100 Million Brazilian Cohort project funded by the Wellcome Trust. Grant code: 202912/B/16/Z.London School of Hygiene and Tropical Medicine. Faculty of Epidemiology and Population Health. London, United Kingdom / University of Oxford. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences. Center for Statistics in Medicine. Oxford, United Kingdom / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil / Universitat Autònoma de Barcelona. Research Group (Idiap Jordi Gol) and Musculoskeletal Research Unit (Fundació IMIM-Parc Salut Mar). Barcelona, Spain.University of Sorocaba. Sorocaba, SP, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil / University of Bahia. Institute of Public Health. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.London School of Hygiene and Tropical Medicine. Faculty of Epidemiology and Population Health. London, United Kingdom.London School of Hygiene and Tropical Medicine. Faculty of Epidemiology and Population Health. London, United Kingdom / University of Oxford. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences. Center for Statistics in Medicine. Oxford, United KingdomFundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil / University of Bahia. Institute of Public Health. Salvador, BA, Brasil / Federal University of Bahia. Department of Statistics. Salvador, BA, Brazil.London School of Hygiene and Tropical Medicine. Faculty of Epidemiology and Population Health. London, United Kingdom / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of intervention or treatment on outcomes. They are also the designs of choice for health technology assessment (HTA). Randomization ensures comparability, in both measured and unmeasured pretreatment characteristics, of individuals assigned to treatment and control or comparator. However, even adequately powered RCTs are not always feasible for several reasons such as cost, time, practical and ethical constraints, and limited generalizability. RCTs rely on data collected on selected, homogeneous population under highly controlled conditions; hence, they provide evidence on efficacy of interventions rather than on effectiveness. Alternatively, observational studies can provide evidence on the relative effectiveness or safety of a health technology compared to one or more alternatives when provided under the setting of routine health care practice. In observational studies, however, treatment assignment is a non-random process based on an individual's baseline characteristics; hence, treatment groups may not be comparable in their pretreatment characteristics. As a result, direct comparison of outcomes between treatment groups might lead to biased estimate of the treatment effect. Propensity score approaches have been used to achieve balance or comparability of treatment groups in terms of their measured pretreatment covariates thereby controlling for confounding bias in estimating treatment effects. Despite the popularity of propensity scores methods and recent important methodological advances, misunderstandings on their applications and limitations are all too common. In this article, we present a review of the propensity scores methods, extended applications, recent advances, and their strengths and limitations.engFrontiers MediaViésConfusãoEficáciaAvaliação de tecnologias em saúdeEscore de propensãoSegurançaSecundárioBiasBonfoundingEffectivenessHealth technology assessmentPropensity scoreSafetySecondaryPropensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advancesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/39607/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALAli M S Propensity Score Methods Front Pharmacol.pdfAli M S Propensity Score Methods Front Pharmacol.pdfapplication/pdf2721230https://www.arca.fiocruz.br/bitstream/icict/39607/2/Ali%20M%20S%20Propensity%20Score%20Methods%20Front%20Pharmacol.pdf1bce7454ce0c8143f5248332ec20f71eMD52TEXTAli M S Propensity Score Methods Front Pharmacol.pdf.txtAli M S Propensity Score Methods Front Pharmacol.pdf.txtExtracted texttext/plain116559https://www.arca.fiocruz.br/bitstream/icict/39607/3/Ali%20M%20S%20Propensity%20Score%20Methods%20Front%20Pharmacol.pdf.txt50d4249a86d927c2b6bfd28559c4265aMD53icict/396072023-03-15 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dc.title.pt_BR.fl_str_mv Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
title Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
spellingShingle Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
Ali, M. Sanni
Viés
Confusão
Eficácia
Avaliação de tecnologias em saúde
Escore de propensão
Segurança
Secundário
Bias
Bonfounding
Effectiveness
Health technology assessment
Propensity score
Safety
Secondary
title_short Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
title_full Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
title_fullStr Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
title_full_unstemmed Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
title_sort Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
author Ali, M. Sanni
author_facet Ali, M. Sanni
Alhambra, Daniel Prieto
Lopes, Luciane Cruz
Ramos, Dandara
Bispo, Nivea
Ichihara, Maria Y.
Pescarini, Julia M.
Williamson, Elizabeth
Fiaccone, Rosemeire L.
Barreto, Mauricio Lima
Smeeth, Liam
author_role author
author2 Alhambra, Daniel Prieto
Lopes, Luciane Cruz
Ramos, Dandara
Bispo, Nivea
Ichihara, Maria Y.
Pescarini, Julia M.
Williamson, Elizabeth
Fiaccone, Rosemeire L.
Barreto, Mauricio Lima
Smeeth, Liam
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ali, M. Sanni
Alhambra, Daniel Prieto
Lopes, Luciane Cruz
Ramos, Dandara
Bispo, Nivea
Ichihara, Maria Y.
Pescarini, Julia M.
Williamson, Elizabeth
Fiaccone, Rosemeire L.
Barreto, Mauricio Lima
Smeeth, Liam
dc.subject.other.pt_BR.fl_str_mv Viés
Confusão
Eficácia
Avaliação de tecnologias em saúde
Escore de propensão
Segurança
Secundário
topic Viés
Confusão
Eficácia
Avaliação de tecnologias em saúde
Escore de propensão
Segurança
Secundário
Bias
Bonfounding
Effectiveness
Health technology assessment
Propensity score
Safety
Secondary
dc.subject.en.pt_BR.fl_str_mv Bias
Bonfounding
Effectiveness
Health technology assessment
Propensity score
Safety
Secondary
description The 100 Million Brazilian Cohort project funded by the Wellcome Trust. Grant code: 202912/B/16/Z.
publishDate 2019
dc.date.issued.fl_str_mv 2019
dc.date.accessioned.fl_str_mv 2020-01-29T18:13:14Z
dc.date.available.fl_str_mv 2020-01-29T18:13:14Z
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dc.identifier.citation.fl_str_mv ALI, M Sanni et al. Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances. Frontiers in Pharmacology, p. 1-19, 2019.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/39607
dc.identifier.issn.pt_BR.fl_str_mv 1663-9812
dc.identifier.doi.none.fl_str_mv 10.3389/fphar.2019.00973
identifier_str_mv ALI, M Sanni et al. Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances. Frontiers in Pharmacology, p. 1-19, 2019.
1663-9812
10.3389/fphar.2019.00973
url https://www.arca.fiocruz.br/handle/icict/39607
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