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M.D. Anderson Cancer Center, United States of America
This half day short course will present statistical concepts and methods related to precision, or ‘personalized’ medicine, which uses individual patient covariates to choose treatment or doses. The topics are drawn from the book, ‘Bayesian Precision Medicine’ published by Chapman and Hall in 2024. To start, the problem of comparing immunotherapy to prayer for treating a severe disease will be discussed. Basic concepts of causal inference will be reviewed, including bias correction methods for analyzing observational data, causal diagrams, with both toy and real-world illustrative examples. Two clinical trial designs will be reviewed that aim to identify optimal subgroup-specific doses or treatments in particular medical settings, each using a utility of a multivariate outcome. The first is a phase 1-2 design that uses the joint utility of five time-to-event outcomes to optimize patient subgroup-specific natural killer cell doses for treating advanced leukemia or lymphoma. The second design does phase 2 treatment screening and selection, illustrated by a randomized three-arm trial to compare targeted agents. Two data analyses that apply Bayesian nonparametric regression models to identify optimal covariate-specific treatments then will be presented. The first analysis uses observational data to identify optimal covariate-specific doses of intravenous busulfan as part of the preparative regimen for allogeneic stem cell transplantation. The second is a re-analysis of a published dataset from a randomized trial, with a joint utility of progression free survival time and total toxicity burden used to choose optimal personalized targeted therapies for advanced breast cancer
