A Bayesian phase II proof-of-concept design for clinical trials with longitudinal endpoints

Existing phase II clinical trial designs focus on a single scalar endpoint, such as a binary, continuous, or survival endpoint. In some clinical trials, such as pain management studies, the efficacy endpoint of interest is measured longitudinally. We propose a Bayesian phase II design for such clinical trials. We model the longitudinal measurement process using Bayesian hierarchical model, where subject-specific trajectory shrinks toward the population trajectory to borrow information across subjects. The Bayesian penalized spline is used to model subject-specific and population trajectories without making strong parametric assumption on their shapes. We use the area under the curve of the trajectory as the summary of the treatment effect over time. The design takes a group sequential approach and takes into account both statistical significance and clinical relevance. Bayesian criteria is proposed to make interim and final decisions based on the evidence of statistical significance and clinical relevance. The proposed design is highly flexible and can accommodate trials with one or multiple longitudinal endpoints, as well as a longitudinal primary endpoint with a secondary endpoint. Simulation study shows that the proposed design is robust with desirable operating characteristics.

Automated summary

This paper proposes a Bayesian phase II design for clinical trials with longitudinal measurement, such as pain management studies. The design utilizes a Bayesian hierarchical model to capture subject-specific trajectory and population trajectory, with a Bayesian penalized spline to model the shapes of these trajectories. The design takes a group sequential approach and considers both statistical significance and clinical relevance in making interim and final decisions.