A threshold analysis assessed the credibility of conclusions from network meta-analysis

Deborah M. Caldwell, A. E. Ades, Sofia Dias, Sarah Watkins, Tianjing Li, Nichole Taske, Bhash Naidoo, Nicky J. Welton

Research output: Contribution to journalArticlepeer-review

Abstract

Objective To assess the reliability of treatment recommendations based on network meta-analysis (NMA). Study Design and Setting We consider evidence in an NMA to be potentially biased. Taking each pairwise contrast in turn, we use a structured series of threshold analyses to ask: (1) “How large would the bias in this evidence base have to be before it changed our decision?” and (2) “If the decision changed, what is the new recommendation?” We illustrate the method via two NMAs in which a Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment for NMAs has been implemented: weight loss and osteoporosis. Results Four of the weight-loss NMA estimates were assessed as “low” and six as “moderate” quality by GRADE; for osteoporosis, six were “low,” nine were “moderate,” and 1 was “high.” The threshold analysis suggests plausible bias in 3 of 10 estimates in the weight-loss network could have changed the treatment recommendation. For osteoporosis, plausible bias in 6 of 16 estimates could change the recommendation. There was no relation between plausible bias changing a treatment recommendation and the original GRADE assessments. Conclusions Reliability judgments on individual NMA contrasts do not help decision makers understand whether a treatment recommendation is reliable. Threshold analysis reveals whether the final recommendation is robust against plausible degrees of bias in the data.

Original languageEnglish (US)
Pages (from-to)68-76
Number of pages9
JournalJournal of Clinical Epidemiology
Volume80
DOIs
StatePublished - Dec 1 2016

Keywords

  • Bias
  • Comparative effectiveness
  • GRADE
  • Health technology assessment
  • Mixed treatment comparison
  • Quality assessment
  • Reliability

ASJC Scopus subject areas

  • Epidemiology

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