Sensitivity to Excluding Treatments in Network Meta-Analysis

• Activity Number: 59
• Type: Topic Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Lifeng Lin and Haitao Chu* and James Hodges
• Companies: University of Minnesota and University of Minnesota, Twin Cities and University of Minnesota
• Keywords: network meta-analysis ; clinical trials ; Bayesian hierarchical model

Abstract

Network meta-analysis (NMA) of randomized clinical trials is increasingly used to combine both direct evidence comparing treatments within individual trials and indirect evidence comparing treatments in 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. Also, 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 event rates. This article examines the influence of treatment exclusion using arm-based and contrast-based NMA. The difference between the two NMA approaches is substantial, and it arises almost entirely because when a treatment