Speaker
Description
Confidence distributions are a frequentist alternative to Bayesian posterior distributions. They summarize the knowledge and uncertainty about an unknown model parameter in the form of a probability distribution on the parameter space, just like a posterior distribution, without assuming that the parameter of interest is a random variable. Although confidence distributions are a relatively old concept, they are not well known and have not been used much until recently.
As part of the EU-PEARL project, two platform-basket trials were developed for neurofibromatosis type I and II, which are rare diseases affecting mainly children. These platform-basket trials were designed as a collection of single-arm proof-of-concept or phase II trials with a binary endpoint, and with the option to include an interim analysis allowing for early stopping in case of projected lack of success.
In this presentation, we provide statistical analysis strategies based on confidence distributions for single-arm proof-of-concept (PoC) or single-arm phase I or phase II studies, and for master protocol trials that are a series of single-arm studies with a binary endpoint. We present analysis rules for the final analysis as well as for interim analyses rules. For interim analyses we focus on rules which allow for early stopping because of projected lack of success at the final analysis, and we use a frequentist predictive distribution to define such rules.
The operating characteristics of our decision rules can be calculated exactly (no simulations required) in the case of a binary endpoint. We show how this can be done and we also compare the performance of these new rules with that of corresponding Bayesian decision rules, or decision rules based on stochastic curtailment.
Reference:
G. Heimann, P. Jacko, and T. Parke, “Using Confidence Distributions in Final and Interim Analyses for Single-Arm Studies or Platform Trials Consisting of Single-Arm Studies”, Statistics in Medicine 44, no. 20-22 (2025): e70251.
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