Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

Howard Hz Thom,Annamaria Cerulli,Luke S Howard,Richard M Nixon,Gorana Capkun

doi:10.1186/s12874-015-0007-0

Howard Hz Thom, Annamaria Cerulli + Show 3 more

Open Access

https://doi.org/10.1186/s12874-015-0007-0

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Abstract

BackgroundNetwork 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).MethodsOur 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.ResultsThrough 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0007-0) contains supplementary material, which is available to authorized users.

Highlights

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
All results were sampled from 250 000 iterations of a single Markov chain Monte Carlo (MCMC) chain following a burn-in of 100 000 iterations
Results of model M3 and M4: NMA of individual patient data (IPD) and aggregate data with covariate adjustments Summary statistics of the results of the application of the covariate adjusted NMA model of Section 2.3, model M3, to combining IPD from the IMPRES trial with aggregate data from the literature are reported in Table 6 and Figure 5, while further parameter estimates are provided in Additional file 4

Summary

Introduction

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. Network meta-analysis (NMA) for indirect mixed treatment comparisons of multiple treatments is a generalization of standard meta-analysis, which is used to combine the results of multiple studies, to the comparison of two or more than treatments This has become a well-established methodology for evidence synthesis [2,3] and is routinely used and recommended by NICE [4,5]. Methods for the inclusion of IPD in pairwise metaanalysis have been developed by Sutton et al [8] and Riley et al [9,10] and these were extended to the network metaanalysis of binary outcomes by Saramago et al [7] and Donegan et al [6] This model can be adapted to continuous outcomes and provides a covariate-adjusted NMA model combining IPD and aggregate data

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