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Treatment comparisons for decision making: facing the problems of sparse and few data

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  • Marta O. Soares
  • Jo C. Dumville
  • A. E. Ades
  • Nicky J. Welton

Abstract

type="main" xml:id="rssa12010-abs-0001"> Advanced evidence synthesis techniques such as indirect or mixed treatment comparisons provide powerful analytic tools to inform decision making. In some cases, however, existing research is limited in quantity and/or existing research data are ‘sparse’. We demonstrate how modelling assumptions in evidence synthesis can be explored in the face of limited and sparse data by using an example where estimates of relative treatment effects were required in a synthesis of the available evidence regarding treatments for grade 3 or 4 pressure ulcers.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssa:v:177:y:2014:i:1:p:259-279
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    File URL: http://hdl.handle.net/10.1111/rssa.2013.177.issue-1
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    Citations

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    Cited by:

    1. Chunhu Shi & Jo C Dumville & Nicky Cullum, 2018. "Support surfaces for pressure ulcer prevention: A network meta-analysis," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    2. 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.
    3. Hampson, G. & Towse, A. & Dreitlein, B. & Henshall, C. & Pearson, S., 2018. "Real World Evidence for Coverage Decisions: Opportunities and Challenges," Research Papers 001997, Office of Health Economics.

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