Master protocol designs (basket, umbrella, platform) allow simultaneous comparison of multiple treatments or disease subgroups. Master protocols can be also designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided in two categories: operational seamless, in which the two phases are separated as two independent studies or inferential seamless, in which the interim analysis is considered as an adaptation of the study. Bayesian designs taking into account correlation between treatments and doses are scarcely studied. Our aim is to propose and compare two Bayesian seamless phase II/III designs (operational and inferential) using a binary endpoint for the first stage and a time-to-event endpoint for the second step.

For the first stage, a Bayesian hierarchical model accounting for multiple doses of multiple treatments while taking partial ordering the correlation structure into account was developed. After treatment and dose selection, based on posterior and predictive probabilities, the results of the first phase were incorporated into prior distributions of a time-to-event model. Extensive simulations were performed in order to compare the robustness and operating characteristics of the two seamless designs depending on several prior variabilities or effective sample sizes.

The inferential seamless has in average better operating characteristics in terms of sample size required and precision. If one which to obtain the same operating characteristics under the operational seamless design, a bigger sample size is needed.

When using stage one data to build the prior distributions for the time-to-event model, it should be done carefully in order to not overpower the posterior distributions and influence trial results. Our proposition allows to avoid this kind of issue.