Affiliation:
1. Department of Periodontology and Operative Dentistry Faculty of Dentistry University Hospital Münster Münster Germany
2. Mohammed bin Rashid University of Medicine and Health Sciences Hamdan bin Mohammed College of Dental Medicine Dubai United Arab Emirates
3. Sir John Walsh Research Institute School of Dentistry University of Otago Dunedin New Zealand
4. School of Dentistry University of Jordan Amman Jordan
5. Department of Economics University of Crete Rethimnon Greece
6. Centre for Craniofacial Development & Regeneration Faculty of Dentistry Oral & Craniofacial Sciences King's College London London UK
7. Department of Orthodontics and Dentofacial Orthopedics Dental School/Medical Faculty University of Bern Bern Switzerland
Abstract
AbstractMeta‐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.