Bayesian Adaptive Optimization of Sedative Dose in Preterm Infants Being Treated for Respiratory Distress Syndrome

• Activity Number: 337
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Peter F. Thall* and Hoang Q. Nguyen and Sarah Zohar and Pierre Maton
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and INSERM UMR 1138 and Service Néonatal, CHC St. Vincent
• Keywords: Adaptive design ; Bayesian design ; Clinical trial ; Decision theory ; Neonatal ; Utility

Abstract

The intubation-surfactant-extubation (INSURE) procedure is used worldwide to treat preterm newborn infants suffering from respiratory distress syndrome, caused by an insufficient amount of the chemical surfactant in the lungs. With INSURE, the infant is intubated, surfactant is administered to the trachea, and the infant is extubated. This improves the infant's ability to breathe and decreases the risk of long-term neurological or motor disabilities. To perform the intubation, the infant first must be sedated, but there is no consensus on what sedative dose is best. This talk presents a Bayesian sequentially adaptive design for a clinical trial to optimize sedative dose given to preterm infants undergoing the INSURE procedure. The design is based on three clinical variables, two efficacy and one adverse, using elicited numerical utilities of the eight possible elementary outcomes. A Bayesian parametric dose-outcome model is assumed. The prior is derived from elicited outcome probabilities. Doses are chosen adaptively for successive cohorts using posterior mean utilities, subject to safety and efficacy constraints. A computer simulation study of the design is presented.