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SummaryAim: To assess the risk of bias (RoB) in a subset of randomized controlled trials (RCTs) published in orthodontic journals using the Cochrane RoB tool and to identify associations between domain RoB assessment and treatment effect estimates. Materials and methods: Fifty consecutive issues of four major orthodontic journals were electronically searched to identify RCTs. The quality of the included studies was assessed using the Cochrane RoB tool, which involves seven domains rated as 'low', 'unclear' or 'high': random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting, and other threats to internal validity. Estimates and confidence intervals (CIs) were recorded or calculated where possible for binary and continuous outcome measures. Meta-regression models were employed to assess the impact of RoB per domain on the magnitude of treatment effect. Results: One hundred and one eligible studies involving 128 pair-wise comparisons were retrieved. Blinding of outcome assessors and incomplete outcome data were frequently judged as 'high' for RoB both for studies with binary and continuous outcome (42.9 and 48.8 per cent, respectively). For binary outcomes, high RoB regarding random sequence generation [odds ratio (OR): 5.97, 95% CI: 2.03, 17.63, P-value: 0.002] and incomplete outcome data (OR: 4.07, 95% CI: 1.03, 16.15, P-value: 0.05) were more likely to provide exaggerated effect estimates. Conclusions: There is a need for improved clinical trial methodology and reporting, in order to avoid inflated associations and erroneous conclusions.

This study indicates that MCI is a weak OIRR predictor. Interpretation of the results needs caution due to the observational nature of the present study.

This study indicates that there is evidence that agenesis status is a strong predictor of MCI, whereas gender is a weak predictor of MCI. Caution should be exercised in interpreting the results because of the observational nature of the present study.

BackgroundClustering effects can be encountered in periodontology and implant dentistry research. The aim of this study was to identify studies with clustering effects published in periodontology and oral implantology specialty journals and to assess the frequency by which clustered designs are correctly accounted for in the statistical analysis.MethodsTen periodontology and oral implantology journals were searched to identify studies with clustering effects published between January 1, 2019 and July 31, 2021. Descriptive statistics and frequency distributions were calculated. Associations between the correct statistical handling of clustering effects and study characteristics were explored.ResultsA total of 695 studies were included of which 45.0% correctly accounted for clustering effects in the statistical analysis. Certain journals (p < 0.01) and animal studies (p < 0.01) had lower odds of correctly accounting for clustering effects in the statistical analysis, whereas per unit increase in impact factor (p < 0.001), involvement of statistician (p < 0.001) and when the study design included either repeated measures only (p < 0.01) or both clustering and repeated measures (p < 0.001) had higher odds. The most commonly used tests were the mixed models or generalized estimating equations (64.2%).ConclusionsGreater awareness of the importance of accounting for clustering effects is required to prevent incorrect inferences being drawn. Incorrect inferences are related to lack of data independence and the artificial inflation of the sample size which can result in statistically significant results which are not genuine. This issue can be further exaggerated in combination with publication bias.