A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
Raices Cruz, Ivette LU ; Troffaes, Matthias C.M. ; Lindström, Johan LU
and Sahlin, Ullrika
LU
(2022)
In Statistics in Medicine
41(17).
p.3365-3379
- Abstract
Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with... (More)
Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.
(Less)
- author
- Raices Cruz, Ivette
LU
; Troffaes, Matthias C.M.
; Lindström, Johan
LU
and Sahlin, Ullrika
LU
- organization
- publishing date
- 2022-07-30
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- imprecise probability, meta-analysis, risk of bias, robust Bayesian analysis
- in
- Statistics in Medicine
- volume
- 41
- issue
- 17
- pages
- 15 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:35487762
- scopus:85128959095
- ISSN
- 0277-6715
- DOI
- 10.1002/sim.9422
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: information This work was supported by the Swedish research council FORMAS through the project “Scaling up uncertain environmental evidence” (219-2013-1271) and the strategic research areas BECC (Biodiversity and Ecosystem Services in a Changing Climate) and MERGE (Modelling the Regional and Global Climate/Earth system). Publisher Copyright: © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
- id
- 27436a78-e256-4763-aebc-0bf03c54f43b
- date added to LUP
- 2022-12-16 20:28:39
- date last changed
- 2024-07-11 12:24:52
@article{27436a78-e256-4763-aebc-0bf03c54f43b,
abstract = {{<p>Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.</p>}},
author = {{Raices Cruz, Ivette and Troffaes, Matthias C.M. and Lindström, Johan and Sahlin, Ullrika}},
issn = {{0277-6715}},
keywords = {{imprecise probability; meta-analysis; risk of bias; robust Bayesian analysis}},
language = {{eng}},
month = {{07}},
number = {{17}},
pages = {{3365--3379}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Statistics in Medicine}},
title = {{A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis}},
url = {{http://dx.doi.org/10.1002/sim.9422}},
doi = {{10.1002/sim.9422}},
volume = {{41}},
year = {{2022}},
}