A Likelihood Ratio Test For The Homogeneity of Between-Study Variance in Network Meta-Analysis

Abstract

Background: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. Methods: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). Results: The type I error rate was well controlled in the Monte Carlo simulation. The homogeneous between-study variance assumption is unrealistic both statistically and practically in the network meta-analysis BRD. The point estimate and conffdence interval of relative effect sizes are strongly inuenced by this assumption. Conclusions: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

Bayesian multivariate network meta‐analysis model for the difference in restricted mean survival times

“…47 Hu et al proposed a likelihood ratio test for the homogenous variance assumption and found significant difference of two heterogeneity parameters between comparisons for non-active control to active treatments and comparisons for active to active treatments. 58 If the homogenous variance assumption does not hold, using the NMA model with the assumption of homogenous variance will influence the point estimates and confidence intervals of treatment effects, 58 and distort the treatment ranking probabilities. 47 Similarly, the assumption of common between-study correlation may be violated in NMA.…”