Gary L. Rosner, Sc.D.
Professor of Biostatistics
The University of Texas M. D. Anderson Cancer Center
Houston, Texas U.S.A.

I will highlight various Bayesian methods we use to design and analyze population-based studies of anticancer therapy.  In particular, we use Bayesian nonparametric models and combine information across studies and patients within studies by hierarchical modeling. 

The specific application concerns predicting the optimal dose for a leukemia patient undergoing high doses of chemotherapy, followed by bone marrow transplantation.  The optimization seeks to find the dose that minimizes the expected loss, where the loss function associates costs when the area under the concentration-time curve (AUC), a measure of systemic exposure, is either below or above the limits of a target range.  By first giving a patient a sub-therapeutic test dose of the anticancer drug, we estimate the patient-specific pharmacokinetics and use this information to predict the patient’s AUC as a function of dose.  The study design uses data from the following sources.  First, we have historical data on leukemia patients who underwent the same high-dose chemotherapy.  These data consist of pharmacokinetics and clinical outcomes.  A subsequent study collected pharmacokinetic information on patients who received a fixed low dose and a non-individualized high-dose of the drug.  In the third study, patients receive a low, test dose of the drug, the same low dose as in the second study.  We fit a pharmacokinetic model to the concentrations of the drug measured in the patient after administration of the test dose to infer the patient-specific parameters in the model of drug disposition.  We determine the optimal dose by averaging a loss function with respect to the predictive distribution for the patient’s AUC as a function of dose.  The patient will then receive the optimal dose, which is the dose that is associated with the smallest expected loss for the patient.  Our design incorporates historical information from the other two studies, along with the current patient’s data, borrowing strength to improve the precision of the prediction.