Abstract #300828

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JSM 2002 Abstract #300828
Activity Number: 72
Type: Contributed
Date/Time: Monday, August 12, 2002 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #300828
Title: Designing Dose Individualization for Transplant Anti-Cancer Therapy Based on Bayesian Population Pharmacokinetic Model
Author(s): Feng Tang*+ and Gary Rosner and Peter Muller
Affiliation(s): U. T. M. D. Anderson Cancer Center and U. T. M. D. Anderson Cancer Center and U. T. M. D. Anderson Cancer Center
Address: 1400 Holcombe Blvd, FC2.3001, Houston, Texas, 77030, USA
Keywords: Nonparametric ; Bayesian hierarchical model ; Pharmacoknetic ; Design
Abstract:

We describe a study design in which the analysis of historical information provides a prior distribution for a future study. In the future study, patients will first receive a very low dose of the drug to allow the care-givers to predict the optimal dose of the same drug. The optimality is based on the patients' systemic exposure as measured by the AUC falling within a prescribed range. A Bayesian hierarchical nonlinear model, with a semi-parametric Dirichlet process mixture prior at the second stage, is used for the analysis of the historical data. Bayesian calibration is carried out to obtain the optimal dose.

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