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The Use of Bayesian Networks to Assess the Quality of Evidence from Research Synthesis: 1

Lookup NU author(s): Dr Gavin Stewart

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

BackgroundThe grades of recommendation, assessment, development and evaluation (GRADE) approach is widely implemented in systematic reviews, health technology assessment and guideline development organisations throughout the world. A key advantage to this approach is that it aids transparency regarding judgments on the quality of evidence. However, the intricacies of making judgments about research methodology and evidence make the GRADE system complex and challenging to apply without training.MethodsWe have developed a semi-automated quality assessment tool (SAQAT) vertical bar based on GRADE. This is informed by responses by reviewers to checklist questions regarding characteristics that may lead to unreliability. These responses are then entered into the Bayesian network to ascertain the probabilities of risk of bias, inconsistency, indirectness, imprecision and publication bias conditional on review characteristics. The model then combines these probabilities to provide a probability for each of the GRADE overall quality categories. We tested the model using a range of plausible scenarios that guideline developers or review authors could encounter.ResultsOverall, the model reproduced GRADE judgements for a range of scenarios. Potential advantages over standard assessment are use of explicit and consistent weightings for different review characteristics, forcing consideration of important but sometimes neglected characteristics and principled downgrading where small but important probabilities of downgrading are accrued across domains.ConclusionsBayesian networks have considerable potential for use as tools to assess the validity of research evidence. The key strength of such networks lies in the provision of a statistically coherent method for combining probabilities across a complex framework based on both belief and evidence. In addition to providing tools for less experienced users to implement reliability assessment, the potential for sensitivity analyses and automation may be beneficial for application and the methodological development of reliability tools.


Publication metadata

Author(s): Stewart GB, Higgins JPT, Schunemann H, Meader N

Publication type: Article

Publication status: Published

Journal: PLoS One

Year: 2015

Volume: 10

Issue: 4

Online publication date: 02/04/2015

Acceptance date: 10/11/2014

Date deposited: 06/05/2016

ISSN (print): 1932-6203

Publisher: Public Library of Science

URL: http://dx.doi.org/10.1371/journal.pone.0114497

DOI: 10.1371/journal.pone.0114497


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