A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis

• Published in: Systematic reviews Vol. 10; no. 1; pp. 310 - 8
• Main Authors: Hu, Dapeng, Wang, Chong, O’Connor, Annette M.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.12.2021; BMC
• Subjects: Animals; Antibiotics; Between-study variance; Biomedicine; Cattle; Computer Simulation; Estimates; Health Sciences; Heterogeneity; Humans; Hypothesis testing; Likelihood Functions; Medicine; Medicine & Public Health; Meta-analysis; Methodology; Models, Statistical; Network meta-analysis; Network Meta-Analysis as Topic; Research Design; Respiratory diseases; Statistics for Life Sciences; Vaccines; Between-study variance; Hypothesis testing; Heterogeneity; Network meta-analysis
• ISSN: 2046-4053; 2046-4053
• DOI: 10.1186/s13643-021-01859-3

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. We found statistical evidence ( p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced 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.