Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials. (July 2013)

• Record Type: Journal Article
• Authors: Dias, Sofia; Welton, Nicky J.; Sutton, Alex J.; Caldwell, Deborah M.; Lu, Guobing; Ades, A. E.
• Is Part Of: Medical decision making. Volume 33:Number 5(2013)
• Page Start: 641
• Page End: 656
• Publication Date: 2013-07
• Subjects: Network meta-analysis -- inconsistency -- indirect evidence -- Bayesian; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X12455847
• Languages: English
• ISSNs: 0272-989X

Abstract

Inconsistency can be thought of as a conflict between "direct" evidence on a comparison between treatments B and C and "indirect" evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.