Conference Agenda

Confidence distributions provide a holistic summary of the information that the data contains about a parameter in a statistical model, expressed using a probability distribution over the parameter space. They provide a frequentist analogue of Bayesian posterior distributions, but without the requirement to specify a prior distribution. In randomised clinical trials, confidence distributions are particularly useful for summarising the evidence for a treatment effect, allowing the strength of evidence to be quantified using a confidence statement such as Conf(Benefit)=92%. In this talk, I will review the application of confidence distributions to clinical trials using various case studies and then present promising lines of future research. Confidence distributions are useful at all stages of a clinical trial, from design to monitoring to analysis. They are particularly promising for adaptive designs, where they can be used to adapt design features such as the randomisation probabilities, the sample size or the available treatments. These confidence-adaptive designs provide various advantages over other types of adaptive designs, by making use of connections with long-standing frequentist group sequential theory that allows less reliance on extensive simulation. In some contexts, confidence distributions may provide the advantages of a Bayesian analysis but with less complexity and sensitivity to assumptions. They have recently found their way into major medical journals and are a promising new tool for clinical biostatisticians.