Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods
1 Institute for Heart and Lung Health, Faculty of Medicine, University of British Columbia, 317 – 2194 Health Sciences Mall (Woodward Instructional Resource Centre), Vancouver, Canada V6T 1Z3
2 Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, 2405 Wesbrook Mall, Vancouver, Canada V6T 1Z3
3 Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Institute, University of British Columbia, 7th Floor, 828 West 10th Avenue, Research Pavilion, Vancouver, Canada V5Z 1M9
4 Faculty of Health Sciences, University of Ottawa, 451, Smyth Road, Ottawa, Canada K1H 8M5
5 School of Public and Population Health, University of British Columbia, 2206 East Mall, Vancouver, Canada V6T 1Z3
Trials 2014, 15:201 doi:10.1186/1745-6215-15-201Published: 3 June 2014
Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence.
We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions.
In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods.
The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes.