Cargando…

Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance

BACKGROUND: Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective...

Descripción completa

Detalles Bibliográficos
Autores principales: McCarron, C. Elizabeth, Pullenayegum, Eleanor M., Thabane, Lehana, Goeree, Ron, Tarride, Jean-Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189931/
https://www.ncbi.nlm.nih.gov/pubmed/22016772
http://dx.doi.org/10.1371/journal.pone.0025635
_version_ 1782213528775557120
author McCarron, C. Elizabeth
Pullenayegum, Eleanor M.
Thabane, Lehana
Goeree, Ron
Tarride, Jean-Eric
author_facet McCarron, C. Elizabeth
Pullenayegum, Eleanor M.
Thabane, Lehana
Goeree, Ron
Tarride, Jean-Eric
author_sort McCarron, C. Elizabeth
collection PubMed
description BACKGROUND: Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs. METHODOLOGY/PRINCIPAL FINDINGS: Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models. CONCLUSIONS/SIGNIFICANCE: Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses.
format Online
Article
Text
id pubmed-3189931
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-31899312011-10-20 Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance McCarron, C. Elizabeth Pullenayegum, Eleanor M. Thabane, Lehana Goeree, Ron Tarride, Jean-Eric PLoS One Research Article BACKGROUND: Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs. METHODOLOGY/PRINCIPAL FINDINGS: Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models. CONCLUSIONS/SIGNIFICANCE: Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses. Public Library of Science 2011-10-10 /pmc/articles/PMC3189931/ /pubmed/22016772 http://dx.doi.org/10.1371/journal.pone.0025635 Text en McCarron et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
McCarron, C. Elizabeth
Pullenayegum, Eleanor M.
Thabane, Lehana
Goeree, Ron
Tarride, Jean-Eric
Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title_full Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title_fullStr Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title_full_unstemmed Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title_short Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance
title_sort bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189931/
https://www.ncbi.nlm.nih.gov/pubmed/22016772
http://dx.doi.org/10.1371/journal.pone.0025635
work_keys_str_mv AT mccarroncelizabeth bayesianhierarchicalmodelscombiningdifferentstudytypesandadjustingforcovariateimbalancesasimulationstudytoassessmodelperformance
AT pullenayegumeleanorm bayesianhierarchicalmodelscombiningdifferentstudytypesandadjustingforcovariateimbalancesasimulationstudytoassessmodelperformance
AT thabanelehana bayesianhierarchicalmodelscombiningdifferentstudytypesandadjustingforcovariateimbalancesasimulationstudytoassessmodelperformance
AT goereeron bayesianhierarchicalmodelscombiningdifferentstudytypesandadjustingforcovariateimbalancesasimulationstudytoassessmodelperformance
AT tarridejeaneric bayesianhierarchicalmodelscombiningdifferentstudytypesandadjustingforcovariateimbalancesasimulationstudytoassessmodelperformance