A design-by-treatment interaction model for network meta-analysis with random inconsistency effects.
Network meta-analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of 'inconsistency' or 'incoherence', where direct evidence and indirect evidence are not in agreement. Here, we develop a random-effects implementation of the recently proposed design-by-treatment interaction model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I(2) statistics to quantify the impact of the between-study heterogeneity and the inconsistency. We apply our model to two examples.
MRC Biostatistics Unit, Cambridge, U.K.
Dan
D
Jackson
Jessica K
JK
Barrett
Stephen
S
Rice
Ian R
IR
White
Julian P T
JP
Higgins
D001499:Bayes Theorem; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D020370:Osteoarthritis, Knee / Q000503:physiopathology / Q000628:therapy; D010146:Pain / Q000517:prevention & control; D012107:Research Design; D016540:Smoking Cessation / Q000379:methods; D012984:Software; D016896:Treatment Outcome
inconsistency; mixed treatment comparisons; multiple treatments meta-analysis; network meta-analysis; sensitivity analysis