![]() Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. ![]() However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. ![]() Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. Meta-analysis is a valuable tool for combining evidence from multiple studies.
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