Deborah Ashby1, Say-Beng Tan. 1. Wolfson Institute of Preventive Medicine, Queen Mary, University of London, London, UK. d.ashby@qmul.ac.uk
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.