Background: Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct
comparisons of two or more treatments for the management of disease by combining evidence from multiple
studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to
randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include
single-arm, before-and-after, observational studies to complete these disconnected networks. We illustrate the
method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH).
Methods: Our method uses a random effects model for placebo improvements to include single-arm observational
studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted
continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm
RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH
combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and
single-arm observational studies, both identified by a systematic review.
Results: Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on
therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT
evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were
inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice.
Conclusions: Our method for the inclusion of single-arm observational studies allows the performance of indirect
comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs.
We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method
could be used in similar situations where treatment comparisons have not been possible due to restrictions to RCT
evidence and where a mixture of aggregate data and IPD are available.