A case study evaluating the effect of clustering, publication bias, and heterogeneity on the meta-analysis estimates in implant dentistry.

Faggion, Clovis Mariano; Atieh, Momen A; Tsagris, Michail; Seehra, Jadbinder; Pandis, Nikolaos (2024). A case study evaluating the effect of clustering, publication bias, and heterogeneity on the meta-analysis estimates in implant dentistry. European journal of oral sciences, 132(1), e12962. Wiley 10.1111/eos.12962

Preview

Text European_J_Oral_Sciences_-_2023_-_Faggion_-_A_case_study_evaluating_the_effect_of_clustering_publication_bias_and.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC). Download (1MB) | Preview

Meta-analyses may provide imprecise estimates when important meta-analysis parameters are not considered during the synthesis. The aim of this case study was to highlight the influence of meta-analysis parameters that can affect reported estimates using as an example pre-existing meta-analyses on the association between implant survival and sinus membrane perforation. PubMed was searched on 7 July 2021 for meta-analyses comparing implant failure in perforated and non-perforated sinus membranes. Primary studies identified in these meta-analyses were combined in a new random-effects model with odds ratios (ORs), confidence intervals (CIs), and prediction intervals reported. Using this new meta-analysis, further meta-analyses were then undertaken considering the clinical, methodological, and statistical heterogeneity of the primary studies, publication bias, and clustering effects. The meta-analyses with the greatest number and more homogeneous studies provided lower odds of implant failure in non-perforated sites (OR 0.49, 95 % CI = [0.26, 0.92]). However, when considering heterogeneity, publication bias, and clustering (number of implants), the confidence in these results was reduced. Interpretation of estimates reported in systematic reviews can vary depending on the assumptions made in the meta-analysis. Users of these analyses need to carefully consider the impact of heterogeneity, publication bias, and clustering, which can affect the size, direction, and interpretation of the reported estimates.

Interest & Impact

Downloads

15 since deposited on 04 Dec 2023
15 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item