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Data analysis used SPM99 and random effects approach.
Variation between studies was taken into account by using a random effects approach (9).
While ANOVA can also be used to compare any number of schools, the random effects approach has a number of advantages over fixed effects models.
Fixed effects are often used in situations in which a key assumption of the random effects approach is thought to be implausible.
A random effects approach using inverse weights resulted in a mean effect size estimate of g = −0.33 (lower scores indicate less weight bias) for both attitudes and beliefs.
As in model (1), mandatory generic substitution, regressive mark-ups, claw-backs and the use of cost effectiveness analysis have a negative and statistically significant effect on originator prices in both the fixed effects and random effects approach.
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16 Although a random-effects approach explicitly models between-study heterogeneity, it does not explain it.
Bipat et al [ 11] discussed a multivariate random-effects approach for meta-analysis of cancer staging.
The studies by Yang and Land [ 17, 28] are some examples applying the random-effects approach.
A random-effects approach was used if heterogeneity was detected; otherwise, a fixed-effect approach was adopted.
Meta-analysis is very commonly performed via a random-effects approach, utilizing the normal-normal hierarchical model.
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