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.
|