Drug‐related covariates should be considered in Bayesian network meta‐analysis of pharmacological interventions
Authors: Jie Wei, Lei Fu, Shi‐fang Peng
Journal: Hepatology (2015)

Potential conflict of interest: Nothing to report. To the Editor: We read with deep interest the article by Singh et al.,1 who performed a Bayesian network meta‐analysis combining direct and indirect treatment comparisons to assess the comparative effectiveness of pharmacological agents for the treatment of nonalcoholic steatohepatitis (NASH). The results of this Bayesian network meta‐analysis indicated that pentoxifylline and obeticholic acid can improve fibrosis, while vitamin E, thiazolidinediones, and obeticholic acid can improve ballooning degeneration in patients with NASH. This finding was very useful for the decision‐making process in treating NASH patients. However, there are some worthwhile issues that need to be further explored. First of all, drug‐related covariates should be taken into account in this Bayesian network meta‐analysis. The forest plot indicated that some outcomes (such as ballooning degeneration, steatosis, and lobular inflammation) exhibited heterogeneity within several comparisons, reflecting that there may be some potential covariates. As shown in their Table 1, the same type of pharmacological intervention, such as vitamin E and thiazolidinediones, can involve various drug doses and different follow‐up durations, which could contribute to the heterogeneity. Between‐study heterogeneity indicates the presence of effect modifiers, and the variability in relative treatment effects has a negative impact on the external validity of trial evidence and limits the ability to generalize from the results.2 In addition, treatment duration of the same pharmacological intervention is another critical covariate that should be reported and considered in this Bayesian network meta‐analysis. The authors should consider the relative contribution of true variability and random variation due to biases. Perhaps Bayesian meta‐regression should be conducted, and the result is worthy of expectation. In addition to the concerns highlighted above, some other issues need to be mentioned. Firstly, model fit should also be assessed to assure the reliability of results of this Bayesian network meta‐analysis because a better model fit can reduce the inconsistency between direct and indirect evidence.3 Secondly, we suggest that the authors refer to a checklist4 which is intended for those who review evidence syntheses and who submit syntheses of Bayesian network meta‐analysis for the purpose of providing more reliable evidence. Thirdly, the randomized controlled trial conducted by Harrison et al.5 compared vitamin E plus vitamin C with placebo for the treatment of NASH. They reluctantly concluded that the therapeutic effect came from only vitamin E. Thus, we also suggest that the authors conduct a sensitivity analysis by excluding this trial. In summary, drug‐related covariates should be taken into account and model fit should be assessed in this Bayesian network meta‐analysis to improve the reliability of results for guiding clinical practice. We respect the great contribution of the authors, and we look forward very much to seeing the authors' response to these issues.