Adaptive decision making in a lymphocyte infusion trial

Abstract

We describe an adaptive Bayesian design for a clinical trial of an experimental treatment for patients with hematologic malignancies who initially received an allogeneic bone marrow transplant but subsequently suffered a disease recurrence. Treatment consists of up to two courses of targeted immunotherapy followed by allogeneic donor lymphocyte infusion. The immunotherapy is a necessary precursor to the lymphocyte infusion, but it may cause severe liver toxicity and is certain to cause a low white blood cell count and low platelets. The primary scientific goal is to determine the infusion time that has the highest probability of treatment success, defined as the event that the patient does not suffer severe toxicity and is alive with recovered white blood cell count 50 days from the start of therapy. The method is based on a parametric model accounting for toxicity, time to white blood cell recovery, and survival time. The design includes an algorithm for between-patient immunotherapy dose de-escalation based on the toxicity data and an adaptive randomization among five possible infusion times according to their most recent posterior success probabilities. A simulation study shows that the design reliably selects the best infusion time while randomizing greater proportions of patients to superior infusion times.