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Incorporating adjustments for variability in control group response rates in network meta-analysis: a case study of
Chris Cameron1, Abhishek Varu2, Arthur Lau3
1Data Analytics & Evidence Synthesis, Cornerstone Research Group, Inc., Burlington, ON, Canada. ccameron@cornerstone-research.com.
Background:
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.
Methods:
Bayesian 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.
Results:
ACR 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.
Conclusions:
In 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.
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