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Publication bias adjustment in network meta-analysis: an inverse probability weighting approach using clinical trial registries

Ao Huang,Yi Zhou,Satoshi Hattori

Published 2024 in Unknown venue

ABSTRACT

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle the publication bias issue in the standard pairwise meta-analysis are hard to extend to this area with the complicated data structure and the underlying assumptions for pooling the data. In this paper, we aimed to provide a flexible inverse probability weighting (IPW) framework along with several t-type selection functions to deal with the publication bias problem in the NMA context. To solve these proposed selection functions, we recommend making use of the additional information from the unpublished studies from multiple clinical trial registries. A comprehensive numerical study and a real example showed that our methodology can help obtain more accurate estimates and higher coverage probabilities, and improve other properties of an NMA (e.g., ranking the interventions).

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    Unknown venue

  • Publication date

    2024-01-31

  • Fields of study

    Medicine, Mathematics

  • Identifiers
    arXiv 2402.00239
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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  • No concepts are published for this paper.

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