Hierarchical network meta-analysis models for synthesis of evidence from randomised and non-randomised studies.

ABSTRACT:

Background

With the increased interest in the inclusion of non-randomised data in network meta-analyses (NMAs) of randomised controlled trials (RCTs), analysts need to consider the implications of the differences in study designs as such data can be prone to increased bias due to the lack of randomisation and unmeasured confounding. This study aims to explore and extend a number of NMA models that account for the differences in the study designs, assessing their impact on the effect estimates and uncertainty.

Methods

Bayesian random-effects meta-analytic models, including naïve pooling and hierarchical models differentiating between the study designs, were extended to allow for the treatment class effect and accounting for bias, with further extensions allowing for bias term

SUBMITTER: Hussein H 

PROVIDER: S-EPMC10122363 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature