Abstract
BackgroundThe importance of adjusting for cross-study heterogeneity in control group response rates when conducting network meta-analyses (NMA) was demonstrated using a case study involving a comparison of biologics for the treatment of moderate-to-severe rheumatoid arthritis.MethodsBayesian NMAs were conducted for American College of Rheumatology (ACR) 50 treatment response based upon a set of randomized controlled trials (RCTs) identified by a recently completed systematic review of the literature. In addition to the performance of an unadjusted NMA, a model adjusting for cross-study heterogeneity of control group response rates using meta-regression was fit to the data. Model fit was evaluated, and findings from both analyses were compared with regard to clinical interpretations.ResultsACR 50 response data from a total of 51 RCTs and 16,223 patients were analyzed. Inspection of cross-study variability in control group response rates identified considerable differences between studies. NMA incorporating adjustment for this variability was associated with an average change of 38.1% in the magnitude of the ORs between treatment comparisons, and over 64% of the odds ratio changed by 15% or more. Important changes in the clinical interpretations drawn from treatment comparisons were identified with this improved modeling approach.ConclusionsIn comparing biologics for moderate to severe rheumatoid arthritis, failure to adjust for cross-trial differences in the control arm response rates in NMA can lead to biased estimates of comparative efficacy between treatments.
Highlights
The importance of adjusting for cross-study heterogeneity in control group response rates when conducting network meta-analyses (NMA) was demonstrated using a case study involving a comparison of biologics for the treatment of moderate-to-severe rheumatoid arthritis
Case study: network meta-analyses of biologic therapies for moderate-to-severe rheumatoid arthritis To illustrate the importance of adjusting for cross-study heterogeneity in RA, we present an illustration based on an evidence base derived from a recent Technology Review of interventions from the Canadian Agency for Drugs and Technologies in Health (CADTH) for moderate to severe RA [13]
We focus on an example employing innovator biologic interventions in the main text of this report, while further analyses adding consideration of biosimilars are presented in the Additional file 1
Summary
The importance of adjusting for cross-study heterogeneity in control group response rates when conducting network meta-analyses (NMA) was demonstrated using a case study involving a comparison of biologics for the treatment of moderate-to-severe rheumatoid arthritis. Variability in control group response rate between interventions and studies within NMA can inflate relative estimates of treatment effect for those interventions with values lower than the overall average while biasing against those interventions with higher response rates. Given the common challenge of access to sufficiently large numbers of studies for meta-regression analysis and lack of reporting of many characteristics, [9] the availability of a characteristic such as control group response rate which can indirectly account for variability in multiple measures can be of considerable value
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
