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Stat Med. 2019 Jun 30;38(14):2505-2523. doi: 10.1002/sim.8139. Epub 2019 Mar 20.

Incorporating single-arm evidence into a network meta-analysis using aggregate level matching: Assessing the impact.

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School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Dublin, Ireland.
National Centre for Pharmacoeconomics, St. James's Hospital, Dublin, Ireland.
Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK.
Department of Health Research and Policy Epidemiology, Stanford University School of Medicine, Stanford, California.
School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland.
Department of Mathematics and Statistics, Health Research Institute and MACSI, University of Limerick, Limerick, Ireland.


Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process. We consider matching aggregate level covariates to comparator arms or trials and including this evidence in a network meta-analysis. We consider two methods of matching: (i) we include the chosen matched arm in the data set itself as a comparator for the single-arm trial; (ii) we use the baseline odds of an event in a chosen matched trial to use as a plug-in estimator for the single-arm trial. We illustrate that the synthesis of evidence resulting from such a setup is sensitive to the between-study variability, formulation of the prior for the between-design effect, weight given to the single-arm evidence, and extent of the bias in single-armed evidence. We provide a flowchart for the process involved in such a synthesis and highlight additional sensitivity analyses that should be carried out. This work was motivated by a hepatitis C data set, where many agents have only been examined in single-arm studies. We present the results of our methods applied to this data set.


hepatitis C; hierarchical model; matched arms; network meta-analysis; single arm


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