The challenge of incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods

Cost-effectiveness analyses (CEAs) that use patient-specific data on costs and health outcomes from a randomized controlled trial (RCT) are popular, yet such CEAs are often criticized because they neglect to incorporate evidence external to the trial. Although evidence directly defined on cost and health outcomes is often not transferrable across jurisdictions, evidence on biologic aspects of treatments such as the treatment effect can be transferred, and incorporating such evidence in the CEA can conceivably affect the results. Fully parametric Bayesian evidence synthesis for RCTbased CEAs is possible, but there are challenges involved in parametric modeling of cost and health outcomes and their relation with external evidence. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. It will be attractive to further expand this method for the incorporation of external evidence. To this end, 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. 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.