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Network Meta-Analysis of Disconnected Networks: How Dangerous Are Random Baseline Treatment Effects?

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Title: Network Meta-Analysis of Disconnected Networks: How Dangerous Are Random Baseline Treatment Effects?
Language: English
Authors: Béliveau, Audrey (ORCID 0000-0003-4124-2498), Goring, Sarah, Platt, Robert W., Gustafson, Paul
Source: Research Synthesis Methods. Dec 2017 8(4):465-474.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 10
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Descriptors: Risk, Network Analysis, Meta Analysis, Outcomes of Treatment, Models, Comparative Analysis, Bayesian Statistics, Medical Research, Case Studies, Research Reports
DOI: 10.1002/jrsm.1256
ISSN: 1759-2879
Abstract: In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1256840
Database: ERIC
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  Data: Network Meta-Analysis of Disconnected Networks: How Dangerous Are Random Baseline Treatment Effects?
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  Data: <searchLink fieldCode="AR" term="%22Béliveau%2C+Audrey%22">Béliveau, Audrey</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4124-2498">0000-0003-4124-2498</externalLink>)<br /><searchLink fieldCode="AR" term="%22Goring%2C+Sarah%22">Goring, Sarah</searchLink><br /><searchLink fieldCode="AR" term="%22Platt%2C+Robert+W%2E%22">Platt, Robert W.</searchLink><br /><searchLink fieldCode="AR" term="%22Gustafson%2C+Paul%22">Gustafson, Paul</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Research+Synthesis+Methods%22"><i>Research Synthesis Methods</i></searchLink>. Dec 2017 8(4):465-474.
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated.
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      – SubjectFull: Meta Analysis
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      – TitleFull: Network Meta-Analysis of Disconnected Networks: How Dangerous Are Random Baseline Treatment Effects?
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