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We used a random-effects model for meta analysis.
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So we used random effects model for meta-analysis.
Regardless of the cause, heterogeneity was managed by using a random-effects model for meta-analyses.
Trials were pooled with a random effects model for meta-analysis in the presence of statistical heterogeneity.
In equation (3) we extended our work to a generalised BRMA model for meta-analysis of two proportions.
Firstly, we present a random effects variance shift outlier model RVSOM for the random effects model for meta-analytic data.
This model initially allows the identification of any apparent outliers under the standard random effects model for meta-analysis.
Therefore, a randomised effects model for meta-analysis was applied in order to minimise the effect of clinical heterogeneity.
However, the results also revealed some severe limitations of a normal model for meta-analysis of binary data, as now discussed.
The fixed effect model for meta-analysis was used according to the heterogeneity test (χ2 = 0.04, df = 1, P = 0.84, I2 = 0%).
The overall analysis therefore contained 15 data points with threshold ≥ trace, for which we used the bivariate model for meta-analysis.
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