An Adaptive Design of Initial Therapy for Emergency Department Patients with Heart Failure

• Activity Number: 314
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jing Ning and Sijin Wen*+ and Sean Collins and Donald Arthur Berry
• Companies: The University of Texas MD Anderson Cancer Center and West Virginia University and Vanderbilt University and M.D. Anderson Cancer Center
• Keywords: Adaptive Randomization ; Bayesian ; Clinical Trials ; Heart Failure

Abstract

Finding safe and effective treatments for acute heart failure syndrome (AHFS) is a high priority. We propose a Bayesian response-adaptive randomization trial design for AHFS patients. Baseline information collected for each patient with AHFS prior to randomization includes blood pressure, renal function, and dyspnea severity. The primary outcome is discharge readiness within 24 hours of presentation and no unplanned emergency visits or admissions for acute heart failure within 7 days of discharge. We use a Bayesian logistic regression model to characterize the association between primary outcome and patient profile. The trial updates inferences about treatment effects, patient profiles, and their interactions. We adaptively randomize patients to one of five treatments, basing the randomization probability on the accumulative data from the ongoing trial and fitting results from the regression model. The simulation study shows high probability of selecting the best treatment corresponding to the patient's profile while allocating more patients to the efficacious treatments within the trial. This trial design is ideal for identifying effective AHFS treatments.