Network meta-analysis of randomized clinical trials: Reporting the proper summaries

Jing Zhang, Bradley P. Carlin, James D. Neaton, Guoxing G. Soon, Lei Nie, Robert Kane, Beth A. Virnig, Haitao Chu

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


Background In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures. Purpose We aim to develop NMA to accurately estimate the treatment-specific event rates. Methods A Bayesian hierarchical model is developed to illustrate how treatmentspecific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting. Results In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report. Limitations First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to nonignorable missingness and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons. Conclusions The proposed NMA method can accurately estimate treatmentspecific event rates, RDs, and RRs and is recommended.

Original languageEnglish (US)
Pages (from-to)246-262
Number of pages17
JournalClinical Trials
Issue number2
StatePublished - Apr 2014

Bibliographical note

Funding Information:
H.C. is supported in part by the US NCI 1P01CA142538, NIAID AI103012, and a subcontract from the US FDA. B.P.C. is supported in part by the US NCI 1R01-CA157458-01A1 and NIAID AI103012.


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