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Monitoring of large randomised clinical trials: a new approach with Bayesian methods
M K Parmar1, G O Griffiths, D J Spiegelhalter
1Cancer Division, MRC Clinical Trials Unit, 222 Euston Road, NW1 2DA, London, UK.
Lancet (London, England)
|August 15, 2001
Summary
Bayesian methods for clinical trial monitoring proved valuable, keeping trials open when p-values might have suggested early closure. This approach offers a more intuitive alternative for data-monitoring and ethics committees.
Area of Science:
- Clinical Trials
- Medical Statistics
- Oncology
Background:
- Data-monitoring and ethics committees (DMECs) typically use p-values to guide decisions on continuing patient enrollment in randomized clinical trials.
- Bayesian methods were employed to monitor two randomized controlled trials in lung and head and neck cancer patients during the 1990s.
Purpose of the Study:
- To assess the utility of Bayesian methods in monitoring randomized clinical trials compared to conventional p-value-based approaches.
- To evaluate how Bayesian monitoring influenced decisions regarding trial continuation and early closure.
Main Methods:
- Clinicians' prior beliefs about treatment efficacy were formalized into "enthusiastic" and "sceptical" prior distributions before trials commenced.
- These prior distributions were updated annually with trial data, and DMECs decided if results could persuade either a sceptic or an enthusiast.
Main Results:
- DMECs consistently found insufficient evidence to stop either trial early, regardless of whether results favored the study treatment (continuous hyperfractionated accelerated radiotherapy [CHART]) or showed no difference.
- Neither trial was closed prematurely based on the Bayesian monitoring approach.
- The lung cancer trial might have been stopped early if conventional p-value-based stopping rules had been applied.
Conclusions:
- The Bayesian approach to clinical trial monitoring is simple to implement and easy for DMEC members to understand.
- This method is considered more intuitively appealing than traditional p-value-based monitoring strategies.
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