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

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)259-279
Number of pages21
JournalJournal of the Royal Statistical Society: Series A
Volume177
Issue number1
DOIs
Publication statusPublished - 1 Jan 2014

Structured keywords

  • ConDuCT-II

Keywords

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

Fingerprint Dive into the research topics of 'Treatment comparisons for decision making: Facing the problems of sparse and few data'. Together they form a unique fingerprint.

  • Projects

    Cite this