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Extraction protocol for the manuscript entitled 'Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses'

List of verbatims for the manuscript entitled 'Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses'

Systematic reviews considered in the manuscript entitled 'Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses'

Additional file 1. PRISMA-P 2015 checklist.

Extraction protocol for the research study 'Low awareness of the transitivity assumption in complex networks of interventions: empirical evidence from 356 network meta-analyses'.

A list of systematic reviews with network meta-analysis considered in the research work 'Low awareness of the transitivity assumption in complex networks of interventions: empirical evidence from 356 network meta-analyses'. The systematic reviews have been sorted by publication year in ascending order.

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Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling. We consider several likelihood-based inference methods. Confidence intervals are based on normal or Student-t approximations. We extract an empirical data set of 40 meta-analyses from recent reviews published by the German Institute for Quality and Efficiency in Health Care (IQWiG). Methods are then compared empirically as well as in a simulation study, considering odds-ratio and risk ratio effect sizes. Empirically, a majority of the identified meta-analyses include only 2 studies. In the simulation study, coverage probability is, in the presence of heterogeneity and few studies, below the nominal level for all frequentist methods based on normal approximation, in particular when sizes in meta-analyses are not balanced, but improve when confidence intervals are adjusted. Bayesian methods result in better coverage than the frequentist methods with normal approximation in all scenarios. Credible intervals are empirically and in the simulation study wider than unadjusted confidence intervals, but considerably narrower than adjusted ones. Confidence intervals based on the generalized linear mixed models are in general, slightly narrower than those from other frequentist methods. Certain methods turned out impractical due to frequent numerical problems. In the presence of between-study heterogeneity, especially with unbalanced study sizes, caution is needed in applying meta-analytical methods to few studies, as either coverage probabilities might be compromised, or intervals are inconclusively wide. Bayesian estimation with a sensibly chosen prior for between-trial heterogeneity may offer a promising compromise., Comment: 22 pages