Article type
Abstract
Background: Systematic reviews provide an overview of the available studies on a certain topic. By means of meta-analyses pooled-effect estimates can be calculated if the considered data are sufficiently homogenous. Besides traditional meta-analyses, in which direct head-to-head studies comparing 2 interventions are summarised, indirect comparisons and network meta-analyses are increasingly used.
Objectives: To describe and discuss a checklist for the assessment of published indirect comparisons and network meta-analyses.
Methods: Existing approaches for indirect comparisons and network meta-analyses are presented and explained. The main assumptions and requirements of these methods are described. A checklist for the assessment of published indirect comparisons and network meta-analyses is suggested. By means of examples, different types of indirect comparisons and network meta-analyses are described and the application of the checklist is explained.
Results: Within the framework of systematic reviews indirect comparisons and network meta-analyses enable the estimation of effects without corresponding direct head-to-head studies as well as the simultaneous analysis of networks containing more than 2 interventions. The adequate application of these methods requires strong assumptions. Transparent and detailed documentation is essential for an adequate assessment of published results from indirect comparisons and network meta-analyses.
Conclusions: Indirect comparisons and network meta-analyses represent an important advancement of traditional meta-analyses. However, the underlying assumptions and requirements have to be acknowledged.
Objectives: To describe and discuss a checklist for the assessment of published indirect comparisons and network meta-analyses.
Methods: Existing approaches for indirect comparisons and network meta-analyses are presented and explained. The main assumptions and requirements of these methods are described. A checklist for the assessment of published indirect comparisons and network meta-analyses is suggested. By means of examples, different types of indirect comparisons and network meta-analyses are described and the application of the checklist is explained.
Results: Within the framework of systematic reviews indirect comparisons and network meta-analyses enable the estimation of effects without corresponding direct head-to-head studies as well as the simultaneous analysis of networks containing more than 2 interventions. The adequate application of these methods requires strong assumptions. Transparent and detailed documentation is essential for an adequate assessment of published results from indirect comparisons and network meta-analyses.
Conclusions: Indirect comparisons and network meta-analyses represent an important advancement of traditional meta-analyses. However, the underlying assumptions and requirements have to be acknowledged.