Email updates

Keep up to date with the latest news and content from CERA and BioMed Central.

Open Access Methodology

The impact of incorporating Bayesian network meta-analysis in cost-effectiveness analysis - a case study of pharmacotherapies for moderate to severe COPD

Kristian Thorlund12*, Zafar Zafari3, Eric Druyts45, Edward J Mills15 and Mohsen Sadatsafavi6

Author Affiliations

1 Stanford Prevention Research Center, Stanford University, Stanford, CA, USA

2 Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

3 Collaboration for Outcome Research and Evaluations, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada

4 Department of Experimental Medicine, University of British Columbia, Vancouver, BC, Canada

5 Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

6 Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

For all author emails, please log on.

Cost Effectiveness and Resource Allocation 2014, 12:8  doi:10.1186/1478-7547-12-8

Published: 13 March 2014

Abstract

Objective

To evaluate the impact of using network meta-analysis (NMA) versus pair wise meta-analyses (PMA) for evidence synthesis on key outputs of cost-effectiveness analysis (CEA).

Methods

We conducted Bayesian NMA of randomized clinical trials providing head-to-head and placebo comparisons of the effect of pharmacotherapies on the exacerbation rate in chronic obstructive pulmonary disease (COPD). Separately, the subset of placebo–comparison trials was used in a Bayesian PMA. The pooled rate ratios (RR) were used to populate a decision-analytic model of COPD treatment to predict 10-year outcomes.

Results

Efficacy estimates from the NMA and PMA were similar, but the NMA provided estimates with higher precision. This resulted in similar incremental cost-effectiveness ratios (ICER). Probabilities of being cost-effective at willingness-to-pay thresholds (WTPs) between $25,000 and $100,000 per quality adjusted life year (QALY) varied considerably between the PMA- and NMA-based approaches. The largest difference in the probabilities of being cost-effective was observed at a WTP of approximately $40,000/QALY. At this threshold, with the PMA-based analysis, ICS, LAMA and placebo had a 43%, 30, and 18% probability of being the most cost-effective. By contrast, with the NMA based approach, ICS, LAMA, and placebo had a 56%, 19%, and 21% probability of being cost-effective. For larger WTP thresholds the probability of LAMA being the most cost-effective became higher than that of ICS. Under the PMA-based analyses the cross-over occurred at a WTP threshold between $60,000/QALY-$65,000/QALY, whereas under the NMA-based approach, the cross-over occurred between $85,000/QALY-$90,000/QALY.

Conclusion

Use of NMAs in CEAs is feasible and, as our case study showed, can decrease uncertainty around key cost-effectiveness measures compared with the use of PMAs. The approval process of health technologies in many jurisdictions requires estimates of comparative efficacy and cost-effectiveness. NMAs play an increasingly important role in providing estimates of comparative efficacy. Their use in the CEAs therefore results in methodological consistency and reduced uncertainty.

Keywords:
Meta-analysis; Multiple treatment comparison; Bayesian analysis; Cost-effectiveness