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Bayesian network meta-analysis combines both direct and indirect evidence for multiple treatments comparisons to estimate the interrelations across all treatments; and its usefulness has been shown in many previous studies on various medical conditions and interventions.
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Network meta-analysis for multiple treatment comparisons has been a major development in evidence synthesis methodology.
Such comparisons are present in multitude in more complex indirect comparisons and multiple treatment comparisons (MTC).
We used an extension of frequentist random effects models for mixed multiple treatment comparisons.
The network meta-analysis was performed using an extension of frequentist random effects models for mixed multiple treatment comparisons.
This Bayesian network meta-analysis can integrate direct evidence with indirect evidence from multiple treatment comparisons to estimate the interrelations across all treatments.
We used an extension of multivariable Bayesian hierarchical random effects models for mixed multiple treatment comparisons with minimally informative prior distributions.
39 Where studies involved multiple treatment comparisons with a single control, we treated each comparison separately, and we avoided double counting controls by assigning half the controls at random to each comparison.
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