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Network meta-regression will be conducted using random effects network meta-regression models to examine potential effect moderators such as size of mesh, number of tack, follow-up and sample size.
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Sensitivity analysis was conducted using random effects model.
Therefore, analyses were conducted using random effects models.
Meta-analysis was conducted using random effects models and inverse variance methods to weight effect estimates.
All analyses were conducted using random effects models, a type of repeated-measures analysis.
All analysis was conducted using random intercept linear regression (xt reg in Stata).
A cross-sectional survey was conducted using random three-stage sampling (figure 1).
Unadjusted longitudinal analyses of the associations between height and BMI and all covariates were conducted using random effects models, including random intercepts and slopes.
Categorical and dose response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [ 18].
Meta-analyses were conducted using random-effect variance and DerSimonian-Laird estimator (DerSimonian & Laird 1986) in the Metafor package (Viechtbauer 2010).
Meta-analyses were conducted using random-effects models as the underlying rationale of random-effects models may be more appropriate when pooling heterogeneous data, while fixed and random-effects models produce the same result if data are homogenous.
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