Using evidence from randomised controlled trials in economic models: What information is relevant and is there a minimum amount of sample data required to make decisions?

Evidence from randomised controlled trials (RCTs) is used to support regulatory approval and reimbursement decisions. I discuss how these decisions are typically made and argue that the amount of sample data and regulatory authorities concerns over multiplicity are irrelevant when making reimbursement decisions. Decision analytic models (DAMs) are usually necessary to meet the requirements of an economic evaluation. DAMs involve inputs relating to health benefits and resource use that represent unknown true population parameters. Evidence about parameters may come from a variety of sources including RCTs, and uncertainty about parameters is represented by their joint posterior distribution. Any impact of multiplicity is mitigated through the prior distribution. I illustrate our perspective with three examples: the estimation of a treatment effect on a rare event; the number of RCTs available in a meta-analysis; and the estimation of population mean overall survival. I conclude by recommending that reimbursement decisions should be followed by an assessment of the value of sample information and the DAM revised as necessary to include any new sample data that may be generated.