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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)2505-2523
Number of pages19
JournalStatistics in Medicine
Issue number14
Early online date20 Mar 2019
DateAccepted/In press - 15 Feb 2019
DateE-pub ahead of print - 20 Mar 2019
DatePublished (current) - 30 Jun 2019


Increasingly, single armed evidence is included in Health Technology Assessment submissions when companies are seeking reimbursement for new drugs. While it is recognised that Randomised Controlled Trials provide a higher standard of evidence, these are not available for many new agents which have been granted licences 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; 1. we include the chosen matched arm in the dataset itself as a comparator for the single arm trial, 2. 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 syntheses 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 the extent of the bias in single armed evidence. We provide a flow chart 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 dataset where many agents have only been examined in single arm studies. We present the results of our methods applied to this dataset.

    Research areas

  • hepatitis C, hierarchical model, matched arms, network meta‐analysis, single arm



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