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When there was heterogeneity (I>30%), we used and compared both random effect and fixed effects models.
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For all models, both random effects and fixed effects models were estimated.
Three types of indirect analyses were submitted by manufacturer in Scotland: a Bucher analysis, a Bayesian network analysis (both random effects) and a frequentist network analysis (fixed effects).
Both random effects and response values were simulated conditional on the parameter estimates, using 1000 samples.
If the I statistic was greater than 0, we reported the results from both random effects and fixed effects models.
Potentially, the effects on mortality of both random effects and unmeasured factors (such as the quality of care) can be teased out.
Within the MLA approaches, the generalised linear mixed model (GLMM) has the advantage of producing estimates of both random effects and fixed effects (hence the term mixed model in GLMM) and it is not incapacitated by missing data.
In our analysis, we will employ the Bayesian method for both random-effect and fixed-effect models.
Meta-analyses were conducted using both random-effects and fixed-effect models.
The Comprehensive Meta-Analysis software (version 2) (Comprehensive Meta Analysis, 2005) was used to perform the analyses based on both random-effects and mixed-effects model.
Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel.
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