Bayesian optimal design in population models for haematologic data.

We introduce a population model to design optimal apheresis schedules to collect blood stem cells from cancer patients. Blood stem cells are collected prior to the patient undergoing high-dose chemoradiotherapy and are returned after this treatment to enable reconstitution of the white blood cell components. Maximizing the number of cells collected in as few apheresis as possible is desirable. We use a longitudinal data model with random effects to describe profiles of individual patients. A hierarchical prior model introduces common mean profiles for patients undergoing different treatments. We find the optimal apheresis schedule for a new patient by minimizing an expected loss over the posterior predictive distribution of the patient's predicted CD34 profile. We implement estimation of the model and solution of the optimal design problem by a simulation approach, which allows us to accommodate arbitrary shapes for the profiles and realistic loss functions that include relative penalties for the number of scheduled stem cell collections and for collecting fewer than a specified target quantity of total collected stem cells.