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Activity Number: 5 - Recent Development on Statistical Methods for Precision Medicine
Type: Invited
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #308042
Title: Bayesian Nonparametric Survival Regression for Optimizing Precision Dosing of Intravenous Busulfan in Allogeneic Stem Cell Transplantation
Author(s): Peter F. Thall* and Yanxun Xu and William Hua and Borje Andersson
Companies: M.D. Anderson Cancer Center and Johns Hopkins University and Johns Hopkins University and M.D. Anderson Cancer Center
Keywords: Precision medicine; Survival analysis; Bayesian nonparametric model; Optimal treatment; Stem cell transplantation; PK-guided dosing

Intravenous busulfan is a standard component of the preparative regimen in allogeneic stem cell transplantation (allosct) for acute leukemia. Systemic busulfan exposure, characterized by the area under the plasma concentration curve, AUC, is strongly associated with clinical outcome. A high AUC is associated with severe toxicities, while a low AUC carries risks of disease recurrence and graft failure. An optimal AUC interval is determined for each patient by giving a preclinical dose. To determine if the optimal AUC interval varies with individual patient characteristics, we developed a method for determining covariate-specific, personalized AUC intervals. We used a Bayesian nonparametric survival regression model based on a dependent Dirichlet process and Gaussian process prior (DDP-GP) to analyze data from 151 allosct patients. The fitted model identified optimal AUC intervals that varied with age and whether the patient was in complete remission at transplant. Simulations showed that the DDP-GP model’s performance compares favorably with several robust alternative models. An R package, DDPGPSurv, for general implementation of the DDP-GP survival regression model is provided.

Authors who are presenting talks have a * after their name.

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