Statistical Heterogeneity in Oral Health Meta-Analyses.

Providing the summary effect size and its uncertainty, a prediction interval, and a measure of statistical heterogeneity constitute good reporting practices in meta-analyses. Popular statistical heterogeneity measures comprise the and statistics. However, researchers often rely unduly on the statistic, using naive categorizations to gauge the extent of heterogeneity, leading to misuses of the meta-analysis models, deficiencies in reporting, and misleading conclusions.

Most meta-analyses in oral health do not have conclusive and robust results.

Objectives: In meta-analyses with few studies, between-study heterogeneity is poorly estimated. The Hartung and Knapp (HK) correction and the prediction intervals can account for the uncertainty in estimating heterogeneity and the range of effect sizes we may encounter in future trials, respectively. The aim of this study was to assess the reported use of the HK correction in oral health meta-analyses and to compare the published reported results and interpretation i) to those calculated using eight heterogeneity estimators and the HK adjustment ii) and to the prediction intervals (PIs).

Do pooled estimates from orthodontic meta-analyses change depending on the meta-analysis approach? A meta-epidemiological study.

Background: In meta-analyses involving a few trials, appropriate measures should be employed to assess between-study heterogeneity. When the number of studies is less than five and heterogeneity is evident, the Hartung and Knapp (HK) correction should be used. The aim of this study was to compare the reported estimates of published orthodontic meta-analyses with the pooled effect size estimates and prediction intervals (PI) calculated using eight heterogeneity estimators and corrected using the HK correction.