Effects of treatment classifications in network meta-analysis

Abstract

Objectives Network meta-analysis is a popular tool to simultaneously compare multiple treatments and improve treatment effect estimates. However, no widely accepted guidelines are available to classify the treatment nodes in a network meta-analysis, and the node-making process was often insufficiently reported. We aim at empirically examining the impact of different treatment classifications on network meta-analysis results. Methods We collected nine published network meta-analyses with various disease outcomes; each contained some similar treatments that may be lumped. The Bayesian random-effects model was applied to these network meta-analyses before and after lumping the similar treatments. We estimated the odds ratios and their 95% credible intervals in the original and lumped network meta-analyses. We used the adjusted deviance information criterion to assess the model performance in the lumped network meta-analyses, and used the ratios of credible interval lengths and ratios of odds ratios to quantitatively evaluate the estimates’ changes due to lumping. In addition, the unrelated mean effect model was applied to examine the extents of evidence inconsistency. Results The estimated odds ratios of many treatment comparisons had noticeable changes due to lumping; many of their precisions were substantially improved. The deviance information criterion values reduced after lumping similar treatments in seven (78%) network meta-analyses, indicating better model performance. Substantial evidence inconsistency was detected in only one network meta-analysis. Conclusions Different ways of classifying treatment nodes may substantially affect network meta-analysis results. Including many insufficiently compared treatments and analysing them as separate nodes may not yield more precise estimates. Researchers should report the node-making process in detail and investigate the results’ robustness to different ways of classifying treatments.