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Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis

Author

Listed:
  • Hugo Pedder

    (Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK)

  • Sofia Dias

    (Centre for Reviews and Dissemination, University of York, York, North Yorkshire, UK)

  • Meg Bennetts

    (Pharmacometrics, Pfizer Ltd, Sandwich, Kent, UK)

  • Martin Boucher

    (Pharmacometrics, Pfizer Ltd, Sandwich, Kent, UK)

  • Nicky J. Welton

    (Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK)

Abstract

Background Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks. Methods We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an “augmented†network connected by adding studies or treatments back into the data set. Results In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04–1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence. Conclusions Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Suggested Citation

  • Hugo Pedder & Sofia Dias & Meg Bennetts & Martin Boucher & Nicky J. Welton, 2021. "Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis," Medical Decision Making, , vol. 41(2), pages 194-208, February.
  • Handle: RePEc:sae:medema:v:41:y:2021:i:2:p:194-208
    DOI: 10.1177/0272989X20983315
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    References listed on IDEAS

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    1. Marta O. Soares & Jo C. Dumville & A. E. Ades & Nicky J. Welton, 2014. "Treatment comparisons for decision making: facing the problems of sparse and few data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 259-279, January.
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