Where's the utility in Bayesian data-monitoring of clinical trials?

Data monitoring is now an established part of good practice in clinical trials. Bayesian procedures for data-monitoring of treatment trials have been proposed and used, but sometimes without explicit consideration of utilities. A natural statistical framework for evidence-based medicine is a Bayesian approach to decision-making that incorporates an integrated summary of the available evidence and associated uncertainty with assessment of utilities.

We explore this approach to data monitoring, explicitly addressing separately the individual, scientific and public health perspectives. The Data Monitoring Committee's decision can then be thought of as a weighted combination of these perspectives. These ideas are illustrated with a trial of treatments for oesophageal cancer.

For a Bayesian approach without explicit utilities we show that a utility structure is, in fact, implicit, and that it may be viewed as a weighted sum of the individual and scientific utilities.

We argue that explicit consideration of utilities leads to decision-making that is more transparent, and lays foundations for data monitoring of more complex trials.