Developing a Bayesian adaptive design for a phase I clinical trial: a case study for a novel HIV treatment

Abstract

The design of phase I studies is often challenging, because of limited evidence to inform study protocols. Adaptive designs are now well established in cancer but much less so in other clinical areas. A phase I study to assess the safety, pharmacokinetic profile and antiretroviral efficacy of C34‐PEG ₄‐Chol, a novel peptide fusion inhibitor for the treatment of HIV infection, has been set up with Medical Research Council funding. During the study workup, Bayesian adaptive designs based on the continual reassessment method were compared with a more standard rule‐based design, with the aim of choosing a design that would maximise the scientific information gained from the study. The process of specifying and evaluating the design options was time consuming and required the active involvement of all members of the trial's protocol development team. However, the effort was worthwhile as the originally proposed rule‐based design has been replaced by a more efficient Bayesian adaptive design. While the outcome to be modelled, design details and evaluation criteria are trial specific, the principles behind their selection are general. This case study illustrates the steps required to establish a design in a novel context. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd