Meta-analyses of randomized evidence may include published, unpublished, and updated data in an ongoing estimation process that continuously accommodates more data.
Synthesis may be performed either with group data or with meta-analysis of individual patient data (MIPD).
Although MIPD with updated data is considered the gold standard of evidence, there is a need for a careful study of the impact different sources of data have on a meta-analysis and of the change in the treatment effect estimates over sequential information steps.
Unpublished data and late-appearing data may be different from early-appearing data.
Updated information after the end of the main study follow-up may be affected by cross-overs, missing information, and unblinding.
The estimated treatment effect may thus depend on the completeness and updating of the available evidence.
To address these issues, we present recursive cumulative meta-analysis (RCM) as an extension of cumulative meta-analysis.
Recursive cumulative meta-analysis is based on the principle of recalculating the results of a cumulative meta-analysis with each new or updated piece of information and focuses on the evolution of the treatment effect as a more complete and updated picture of the evidence becomes available.
An examination of the perturbations of the cumulative treatment effect over sequential information steps may signal the presence of bias or heterogeneity in a meta-analysis. (...)
Mots-clés Pascal : Métaanalyse, Randomisation, Biais méthodologique, Epidémiologie, Méthodologie, Homme
Mots-clés Pascal anglais : Metaanalysis, Randomization, Methodological bias, Epidemiology, Methodology, Human
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 99-0285060
Code Inist : 002B30A01A1. Création : 16/11/1999.