Ricerc@Sapienza

Interim monitoring is widely adopted in clinical trials to minimize patients' exposure to not sufficiently effective therapies. In this project we aim at developing Bayesian monitoring rules for phase II single-arm trials, that allow to account for crucial issues such as, for instance, the uncertainty in the target response rate, the association between efficacy and toxicity data and the presence of more than two decision makers.

Our interest is mainly focused on bivariate binary outcomes, that represent efficacy and toxicity of the experimental treatment. Most of the Bayesian monitoring rules previously proposed are based on posterior probabilities and exploit marginal models for the two endpoints. We instead suggest to use the joint Dirichlet posterior model to define the experimental treatment as successful, in order to account for the association between efficacy and toxicity. We also aim at evaluating the performance of Bayesian decision rules based on different early stopping boundaries for the posterior probability of interest. As an alternative approach, we will exploit predictive probabilities for interim monitoring of efficacy and toxicity endpoints. The idea is to construct stopping rules based on the predictive probability that the trial will yield a positive outcome, if it continues to the planned end. All these Bayesian methods will be evaluated and compared with already existing procedures in terms of frequentist operating characteristics.

A further purpose of our research group is to adapt the sequential trial monitoring mechanism to the formal framework of a decision-theoretic approach, under the assumption that analysis and design are performed by multiple parties. In this context, the focus is on the construction of a predictive measure of the conflict between their opposite choices. Based on this measure, we aim at establishing stopping rules expressed in terms of the possibility/impossibility of bringing a consensus between the two parties.