Sensitivity to Excluding Treatments in Network Meta-analysis

Abstract

Network meta-analysis (NMA) of randomized controlled trials is increasingly used to combine both direct evidence comparing treatments within trials and indirect evidence comparing treatments across different trials. When the outcome is binary, the commonly used contrast-based NMA methods focus on relative treatment effects such as odds ratios comparing two treatments. As shown in a recent report, when using contrast-based NMA, the impact of excluding a treatment in the network can be substantial, suggesting a methodological limitation. In addition, relative treatment effects are sometimes not sufficient for patients to make decisions. For example, it can be challenging for patients to trade off efficacy and safety for two drugs if they only know the relative effects, not the absolute effects. A recently proposed arm-based NMA, based on a missing-data framework, provides an alternative approach. It focuses on estimating population-averaged treatment-specific absolute effects. This article examines the influence of treatment exclusion empirically using 14 published NMAs, for both arm-based and contrast-based approaches. The difference between these two NMA approaches is substantial, and it is almost entirely due to single-arm trials. When a treatment is removed from a contrast-based NMA, it is necessary to exclude other treatments in two-arm studies that investigated the excluded treatment; such exclusions are not necessary in arm-based NMA, leading to substantial gain in performance.

The Impact of Excluding Trials from Network Meta-Analyses – An Empirical Study

“…The impact of exclusion of treatments arms was investigated in Mills et al [8] and Lin et al [9] empirically and substantial influence was found, whereas the impact of exclusion of trials has not been explored before. In this paper we empirically studied this impact using 20 published networks and documented that exclusion of trials can sometimes affect the estimation of treatment effects substantially.…”

“…They consequently stated that selection of treatment arms should be carefully considered when applying NMAs. Another publication by Lin et al [9] further explored the sensitivity to excluding treatments using both the armed-based (AB) [1] and contrast-based (CB) [2] NMA approaches. They found that when a treatment was removed under the CB framework, it was also necessary to exclude the other treatment in two-arm studies that investigated the excluded treatment, while such additional exclusions were not necessary in the AB framework.…”

Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package

“…Moreover, the arm-based method can use information contained in single-arm studies, in which only one treatment group is available or of interest. Single-arm studies cannot be included in the contrast-based model but they may provide valuable information for treatment comparisons and enhance the robustness of a network meta-analysis (Lin et al 2016a). Although the arm-based approach has many advantages, the convergence of Markov chain Monte Carlo (MCMC) algorithms for parameter estimation may be slower compared with the contrast-based approach.…”

Bayesian multivariate meta‐analysis of multiple factors

“…[13][14][15] The multivariate meta-analysis models have been applied to several areas, such as metaanalysis of diagnostic tests, [16][17][18] meta-analysis of multiple outcomes, 19,20 and network meta-analysis of mixed treatment comparisons. [21][22][23][24][25][26] Mixed treatment comparisons use both direct and indirect evidence of treatment contrasts to synthesize the comparisons between multiple treatments; its focus differs from MVMA-MF, because MVMA-MF is interested in estimating the effect of multiple factors, not the contrasts between them.…”