Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances
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 cha...
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Frontiers Media S.A.
2019-09-01
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Series: | Frontiers in Pharmacology |
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Online Access: | https://www.frontiersin.org/article/10.3389/fphar.2019.00973/full |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M Sanni Ali M Sanni Ali M Sanni Ali Daniel Prieto-Alhambra Daniel Prieto-Alhambra Luciane Cruz Lopes Dandara Ramos Nivea Bispo Maria Y. Ichihara Maria Y. Ichihara Julia M. Pescarini Elizabeth Williamson Rosemeire L. Fiaccone Rosemeire L. Fiaccone Rosemeire L. Fiaccone Mauricio L. Barreto Mauricio L. Barreto Liam Smeeth Liam Smeeth |
spellingShingle |
M Sanni Ali M Sanni Ali M Sanni Ali Daniel Prieto-Alhambra Daniel Prieto-Alhambra Luciane Cruz Lopes Dandara Ramos Nivea Bispo Maria Y. Ichihara Maria Y. Ichihara Julia M. Pescarini Elizabeth Williamson Rosemeire L. Fiaccone Rosemeire L. Fiaccone Rosemeire L. Fiaccone Mauricio L. Barreto Mauricio L. Barreto Liam Smeeth Liam Smeeth Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances Frontiers in Pharmacology bias confounding effectiveness health technology assessment propensity score safety |
author_facet |
M Sanni Ali M Sanni Ali M Sanni Ali Daniel Prieto-Alhambra Daniel Prieto-Alhambra Luciane Cruz Lopes Dandara Ramos Nivea Bispo Maria Y. Ichihara Maria Y. Ichihara Julia M. Pescarini Elizabeth Williamson Rosemeire L. Fiaccone Rosemeire L. Fiaccone Rosemeire L. Fiaccone Mauricio L. Barreto Mauricio L. Barreto Liam Smeeth Liam Smeeth |
author_sort |
M Sanni Ali |
title |
Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances |
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 |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Pharmacology |
issn |
1663-9812 |
publishDate |
2019-09-01 |
description |
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. |
topic |
bias confounding effectiveness health technology assessment propensity score safety |
url |
https://www.frontiersin.org/article/10.3389/fphar.2019.00973/full |
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doaj-fddb553603cc453f9bc37743dce496012020-11-25T02:05:23ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122019-09-011010.3389/fphar.2019.00973466792Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent AdvancesM Sanni Ali0M Sanni Ali1M Sanni Ali2Daniel Prieto-Alhambra3Daniel Prieto-Alhambra4Luciane Cruz Lopes5Dandara Ramos6Nivea Bispo7Maria Y. Ichihara8Maria Y. Ichihara9Julia M. Pescarini10Elizabeth Williamson11Rosemeire L. Fiaccone12Rosemeire L. Fiaccone13Rosemeire L. Fiaccone14Mauricio L. Barreto15Mauricio L. Barreto16Liam Smeeth17Liam Smeeth18Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United KingdomNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Center for Statistics in Medicine (CSM), University of Oxford, Oxford, United KingdomCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Center for Statistics in Medicine (CSM), University of Oxford, Oxford, United KingdomGREMPAL Research Group (Idiap Jordi Gol) and Musculoskeletal Research Unit (Fundació IMIM-Parc Salut Mar), Universitat Autònoma de Barcelona, Barcelona, SpainUniversity of Sorocaba–UNISO, Sorocaba, São Paulo, BrazilCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilInstitute of Public Health, Federal University of Bahia (UFBA), Salvador, BrazilCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United KingdomCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilInstitute of Public Health, Federal University of Bahia (UFBA), Salvador, BrazilDepartment of Statistics, Federal University of Bahia (UFBA), Salvador, BrazilCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilInstitute of Public Health, Federal University of Bahia (UFBA), Salvador, BrazilFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United KingdomCentre for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Osvaldo Cruz, Salvador, BrazilRandomized 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.https://www.frontiersin.org/article/10.3389/fphar.2019.00973/fullbiasconfoundingeffectivenesshealth technology assessmentpropensity scoresafety |