Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods

Trials

Stirling Bryan¹  Shawn Aaron³  Carlo Marra²  Mohsen Sadatsafavi⁴ 

[1]School of Public and Population Health, University of British Columbia, 2206 East Mall, 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]Faculty of Health Sciences, University of Ottawa, 451, Smyth Road, Ottawa, Canada K1H 8M5

[4]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

关键词: Statistics-nonparametric;    Clinical trial;    Bayes theorem;    Cost-benefit analysis;   

Others  :  805407
DOI  :  10.1186/1745-6215-15-201

 received in 2013-09-25, accepted in 2014-05-19,  发布年份 2014

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2014 Sadatsafavi et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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