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Between-study heterogeneity plays an important role in random-effects models for meta-analysis.
The use of random-effects models for meta-analysis reflects the assumption of unexplained heterogeneity in findings.
There are no comprehensive rules on when to use random effects or fixed effects models for meta-analysis.
Between-group differences (Qb) in BMI z-score for categorical variables were examined using mixed effects ANOVA-like models for meta-analysis [ 64].
Our method can be generalized to more complex cases where the previous models for meta-analysis are a mixture of under- and over-specified models.
Between-group differences (Qb) in FN and LS BMD for categorical variables were examined using mixed effects ANOVA-like models for meta-analysis [ 77].
Some simple modifications in the SAS procedure proc mixed allow the fitting of mixed models for meta-analytic data from diagnostic studies.
Two recent approaches, including alternate random effects (RE-HE) [ 56] and MANTRA [ 58], have been proposed to address some of the limitations met by traditional FE or RE models for meta-analysis.
We calculated the pooled sensitivity and specificity, diagnostic odds ratio (DORs) and the likelihood ratio et al. based on the bivariate random effect models for meta-analysis of diagnostic test data [ 13].
We used a random-effects model for meta analysis.
So we used random effects model for meta-analysis.
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