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The bivariate model uses a random effect approach for both sensitivity and specificity, which allows for heterogeneity beyond chance as a result of clinical and methodological differences between studies, and the bivariate model is considered as a more valid statistical model for diagnostic meta-analysis [ 16, 17].
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The individual subject data of the localizer scans were incorporated into a group-level analysis using a random effect model.
Where studies are heterogeneous it is more appropriate to use a random effect model [15].
Meta-analysis was completed using a random effect meta-analysis.
A mixed model, using a random effect for participant, will be used to analyse the data.
We used a random effect on inspections batches around a reporting house.
We used a random effect model and explored for sources of inconsistency (I) and heterogeneity.
Longitudinal changes in WBCs were evaluated using a random effect model.
Data were analysed using a random effect procedure (Friston et al., 1999).
We used a random effect model when the statistical test of heterogeneity showed significance.
We combined these estimates using a random effect meta-analysis as well.
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