Disconnected by design: analytic approach in treatment networks having no common comparator.

In a network meta-analysis, comparators of interest are ideally connected either directly or via one or more common comparators. However, in some therapeutic areas, the evidence base can produce networks that are disconnected, in which there is neither direct evidence nor an indirect route for comparing certain treatments within the network. Disconnected networks may occur when there is no accepted standard of care, when there has been a major paradigm shift in treatment, when use of a standard of care or placebo is debated, when a product receives orphan drug designation, or when there is a large number of available treatments and many accepted standards of care. These networks pose a challenge to decision makers and clinicians who want to estimate the relative efficacy and safety of newly available agents against alternatives. A currently recommended approach is to insert a distribution for the unknown treatment effect(s) into a network meta-analysis model of treatment effect. In this paper, we describe this approach along with two alternative Bayesian models that can accommodate disconnected networks. Additionally, we present a theoretical framework to guide the choice between modeling approaches. This paper presents researchers with the tools and framework for selecting appropriate models for indirect comparison of treatment efficacies when challenged with a disconnected framework.