All health care systems that are collectively funded through taxation or insurance make funding and coverage decisions considering value for money. Over the past 25 years or so, more systems have started to use formal cost-effectiveness analysis (CEA) to inform decisions, particularly those relating to the funding of new prescription pharmaceuticals. Inevitably and correctly, decision makers augment CEA with their own judgments about the reliability and relevance of evidence and the consequences of interventions, which may be poorly reflected in CEA outcomes. One important contribution of the greater use of CEA, however, has been to highlight some key principles that should be used in making resource allocation decisions. CEA undertaken alongside randomized controlled trials (RCTs) will often be inconsistent with these principles [1].

RCTs provide highly valuable evidence to support decision making. This is particularly true in the case of relative treatment effects, the estimation of which ideally requires randomization to reduce the risk of selection bias. Trials can provide other valuable information for CEA including data relating to the disease under current treatment(s), as well as resource use and health-related quality-of-life data. ISPOR’s updated task force report on CEA alongside trials provides important guidance on how economic considerations should shape the design of the trials and the methods of data collection [2]. But a trial-based CEA goes beyond the use of evidence collected in RCTs in CEA. Such a study relates only to the population included in the trial, just includes the clinical and resource use evidence from that RCT, compares only those interventions to which patients were randomized, and has a time horizon defined by the trial’s follow-up period. How often are these characteristics consistent with the key criteria for evidence-based decision making?