A utility‐based Bayesian phase I–II design for immunotherapy trials with progression‐free survival end point

Immunotherapy has been hailed as the biggest breakthrough for treating cancer since the first development of chemotherapy. The new features of immunotherapy make the traditional clinical trial paradigm increasingly inefficient and dysfunctional. We propose a Bayesian phase I–II design for immunotherapy trials called BDFIT to find the optimal biological dose (OBD). We jointly model the toxicity outcome, progression‐free survival (PFS) and immune response. PFS and toxicity are used as the primary end points to determine the OBD, whereas the immune response is used as an ancillary end point to screen out futile doses quickly and to predict the PFS when needed. A utility function is formulated to account for the risk–benefit trade‐off and to quantify the desirability of the dose. During the trial, based on accumulating data, the estimates of the model and dose desirability are continuously updated and used to guide the dose assignment and to select the OBD. The simulation study shows that the BDFIT design has desirable operating characteristics.