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An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior–like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials. [ABSTRACT FROM AUTHOR]

Among men, prostate cancer (CaP) is the most common newly diagnosed cancer and the second leading cause of death from cancer. A major issue of very large scale is avoiding both over-treatment and under-treatment of CaP cases. The central challenge is deciding clinical significance or insignificance when the CaP biopsy results are positive but only marginally so. A related concern is deciding how to increase the number of biopsy cores for larger prostates. As a foundation for improved choice of number of cores and improved interpretation of biopsy results, we develop a probability model for the number of positive cores found in a biopsy, given the total number of cores, the volumes of the tumor nodules, and - very importantly - the prostate volume. Also, three applications are carried out: guidelines for the number of cores as a function of prostate volume, decision rules for insignificant versus significant CaP using number of positive cores, and, using prior distributions on total tumor size, Bayesian posterior probabilities for insignificant CaP and posterior median CaP. The model-based results have generality of application, take prostate volume into account, and provide attractive tradeoffs of specificity versus sensitivity. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]