Web of Science: 106 cites, Scopus: 117 cites, Google Scholar: cites,
Propensity score methods in health technology assessment : Principles, extended applications, and recent advances
Ali, M. S. (Centre for Data and Knowledge Integration for Health (CIDACS). Instituto Gonçalo Muniz. Fundação Osvaldo Cruz)
Prieto-Alhambra, Daniel (Institut Hospital del Mar d'Investigacions Mèdiques)
Lopes, L. C. (University of Sorocaba-UNISO)
Ramos, D. (Centre for Data and Knowledge Integration for Health (CIDACS). Instituto Gonçalo Muniz. Fundação Osvaldo Cruz)
Bispo, N. (Centre for Data and Knowledge Integration for Health (CIDACS). Instituto Gonçalo Muniz. Fundação Osvaldo Cruz)
Ichihara, M. Y. (Institute of Public Health. Federal University of Bahia (UFBA))
Pescarini, J. M. (Centre for Data and Knowledge Integration for Health (CIDACS). Instituto Gonçalo Muniz. Fundação Osvaldo Cruz)
Williamson, E. (Faculty of Epidemiology and Population Health. London School of Hygiene and Tropical Medicine)
Fiaccone, R. L. (Department of Statistics. Federal University of Bahia (UFBA))
Barreto, M. L. (Institute of Public Health. Federal University of Bahia (UFBA))
Smeeth, L. (Centre for Data and Knowledge Integration for Health (CIDACS). Instituto Gonçalo Muniz. Fundação Osvaldo Cruz)
Universitat Autònoma de Barcelona

Data: 2019
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article de revisió ; Article ; Versió publicada
Matèria: Bias ; Confounding ; Effectiveness ; Health technology assessment ; Observational study ; Propensity score ; Safety ; Secondary data
Publicat a: Frontiers in Pharmacology, Vol. 10 (2019) , p. 973, ISSN 1663-9812

DOI: 10.3389/fphar.2019.00973
PMID: 31619986


19 p, 2.6 MB

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