Doctor: Aida Bafeta
Title: Epidemiology and methodology of network meta-analyses
Supervisor: Philippe Ravaud
Doctoral school: ED 393 Epidemiology and Biomedical Information Sciences, Université Paris Cité
Date of thesis defense: 16/01/2015
Jury: Pascal Auquier, Bruno Falissard, Stefan Michiels, Florence Tubach, Philippe Ravaud
Summary:
Network meta-analyses allow for estimating all possible pair-wise comparisons between interventions, even some trials are lacking. In the past few years, network meta-analyses have been increasingly used for comparing healthcare interventions. Network meta-analyses are primarily meta-analyses; they are probably subject to the same types of bias and should therefore be performed according to the explicit and rigorous methods used in systematic reviews. However, did not evaluate the presence of essential methodological components of the systematic review process such as conducting a literature search and assessing the risk of bias of individual studies, or the presentation of findings in reports of network meta-analyses.
We assessed whether network meta-analyses follow the key methodological recommendations for reporting and conduct of systematic reviews. We show that the essential methodological components of the systematic review process—conducting a literature search and assessing risk of bias of individual studies—are frequently lacking in reports of network meta-analyses. We assessed how the results of network meta-analyses are reported. We show that the results of network meta-analyses are heterogeneously reported. The author inadequately reported essential component to judge the amount of evidence (that is, the interventions included in the network, direct comparisons between interventions, number of randomised trials and patients for each comparison) and the relative effect sizes between interventions from direct evidence, indirect evidence, and the network meta-analysis. Development of reporting guidelines to assist authors in writing and readers in critically appraising reports of network meta-analyses is timely.
