Should clinical trials include economic endpoints?

Clinical trials are the primary source of data for cost-effectiveness analyses of health interventions with trials increasingly including economic endpoints. However, an elephant in the room might pose the following question: should clinical trials include economic endpoints?

The term ‘endpoint’ refers to an objectively measured change in an outcome, for instance, the ‘change from baseline in blood pressure over a 24-week period’ is an example of a clinical endpoint that may be incorporated into a trial. Clinical trials, aimed at assessing the safety and efficacy of health interventions, are designed to be able to pose and answer these types of questions. Increasingly, economic endpoints, are being included in clinical trials with the aim of providing key input for economic evaluations. An example of an economic endpoint might be a ‘change from baseline in use of medical equipment over a 24-week period’. However, whilst a clinical trial may be able to provide an answer to this question, should we actually be looking to clinical trials for these answers?

Returning to the definition of endpoint as an objectively measured change in an outcome, and using the above example of medical equipment, can the hypothetical clinical trial objectively provide an answer as to the ‘change from baseline in use of medical equipment’ resulting from the intervention under assessment? The answer is yes, the clinical trial would provide an objective answer to this question -but is it the right type of objectivity needed for decision making in healthcare? To answer this question, we need to assess what we mean by ‘objectively measured’ and how we may wish to define it for purposes of economic evaluation.

In order to make decisions concerning resource allocation in healthcare we require data that can answer the questions we pose with the least amount of uncertainty, ideally none. The inclusion of economic endpoints in clinical trials generates uncertainty, for example, due to the short time span of generated evidence which require extrapolation, or the highly controlled trial setting which would render any resource use determined a poor estimate. Therefore, whilst a convenient means of generating data, basing economic evaluations on data derived from clinical trials could potentially lead to erroneous decision making and unfair resource allocation. This, of course, does not preclude the use of trial evidence to highlight the biological benefit of the intervention. Given that the clinical trial is unlikely of generating data without the need for additional studies to further validate these data, it is difficult to argue that economic endpoints should be considered as part of clinical trials.

One response to “Should clinical trials include economic endpoints?”

  1. You make some good points here. Although it can be of interest collecting data for use in economic models in Randomised Controlled Trials (RCTs) it is not always applicable to usual clinical settings, as you point out. This certainly applies to procedures being carried out, which may be more commonly seen in trials than in standard care, and probably also to interactions with health care professionals. However I wonder whether more use could be made of the use of concomitant medication during trials, which may be less affected by trial procedures and can still provide useful data.

    The other point is that, in an RCT, two or more treatments are being compared, so it it is still valid to assess the difference between treatments in the use of resources, which, despite being subject to the various restrictions imposed by trials, could still provide an unbiased estimate of the difference in resource use, with an associated estimate of uncertainty, albeit over a shorter period of time than may be needed for HE modelling. Furthermore, the data can be collected on an individual patient basis, and combined with efficacy and safety data to assess cost-effectiveness (see e.g. Willan and Briggs, Statistical analysis of cost-effectiveness data, Wiley 2006)

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