Bayesian Precision Medicine presents modern Bayesian statistical models and methods for identifying treatments tailored to individual patients using their prognostic variables and predictive biomarkers. The process of evaluating and comparing treatments is explained and illustrated by practical examples, followed by a discussion of causal analysis and its relationship to statistical inference. A wide array of modern Bayesian clinical trial designs are presented, including applications to many oncology trials. The later chapters describe Bayesian nonparametric regression analyses of datasets arising from multistage chemotherapy for acute leukemia, allogeneic stem cell transplantation, and targeted agents for treating advanced breast cancer.
Features:
- Describes the connection between causal analysis and statistical inference
- Reviews modern personalized Bayesian clinical trial designs for dose-finding, treatment screening, basket trials, enrichment, incorporating historical data, and confirmatory treatment comparison, illustrated by real-world applications
- Presents adaptive methods for clustering similar patient subgroups to improve efficiency
- Describes Bayesian nonparametric regression analyses of real-world datasets from oncology
- Provides pointers to software for implementation
Bayesian Precision Medicine is primarily aimed at biostatisticians and medical researchers who desire to apply modern Bayesian methods to their own clinical trials and data analyses. It also might be used to teach a special topics course on precision medicine using a Bayesian approach to postgraduate biostatistics students. The main goal of the book is to show how Bayesian thinking can provide a practical scientific basis for tailoring treatments to individual patients.
1. Evaluating New Treatments
2. Statistics and Causality
3. Precision Dose Optimization
4. Bayesian Basket Trials
5. Precision Randomized Phase II Designs
6. Precision Phase III Designs
7.Enrichment Concepts and Methods
8. Adaptive Enrichment Designs
9. Bayesian Nonparametric Models
10. Evaluating Multistage Treatment Regimes for Acute Leukemia
11. Personalizing Preparative Regimen in Stem Cell Transplantation
12. Utilities for Personalizing Advanced Breast Cancer Treatment
Biography
Peter F. Thall is a global leader in the development and application of Bayesian methods in medical research, with over 300 publications in professional journals. His research interests include Bayesian statistics, clinical trial design, precision medicine, and dynamic treatment regimes.
“With this latest book, Dr. Thall adds to his reputation as one of the most innovative thinkers in the field of adaptive clinical trial design. This book offers a wide variety of cutting-edge methods in Bayesian precision medicine, all explicated in the context of utility-driven designs that can simultaneously evaluate and trade off treatment safety and efficacy. The book’s unification of standard tools for causal inference with Bayesian methods is very welcome, as is its generous collection of case studies, most drawn from the author’s own extensive statistical consulting portfolio. It is a must-read for students and practitioners in biopharmaceutical statistics who want to see the current frontier of individualized complex innovative trial design.”
Bradley P. Carlin, Cencora-PharmaLex, USA
