Abstract
BackgroundCost-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.MethodsWe 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.ResultsIn 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.ConclusionsThe 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.
Highlights
Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such cost-effectiveness analysis (CEA) are criticized because they neglect to incorporate evidence external to the trial
An illustrative example This case study is to demonstrate the operational aspects of implementing the algorithm and is not intended to be a practice in comprehensive evidence synthesis to inform policy
The case study is based on the OPTIMAL trial, a multicenter study evaluating the benefits of combination pharmacological therapy in preventing respiratory exacerbations in patients with chornic, obstructive pulmonary disease (COPD) [32,33]
Summary
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. In parallel-arm RCTs, this can be performed by obtaining a bootstrap sample within each arm of the trial and calculating the mean cost and effectiveness within each arm from the bootstrap sample; repeating this step many times provides a random sample from the joint distribution of arm-specific cost and effectiveness outcomes. This sample can be used to make inference on (such as calculate the confidence or credible interval for) the ICER [5]
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