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

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

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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. © 2013 Royal Statistical Society.
    Original languageEnglish
    Pages (from-to)259-279
    Number of pages20
    JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
    Volume177
    Issue number1
    DOIs
    Publication statusPublished - Jan 2014

    Keywords

    • Elicited evidence
    • Evidence synthesis
    • Mixed treatment comparison
    • Network meta-analysis
    • Observational studies
    • Randomized controlled trials evidence
    • Sparse data

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