Dose-Exposure-Response Modeling: Methods and Practical Implementation [Hardcover]

This thoroughly revised and updated new edition reflects the progress that has been made in dose-exposure-response (DER) modelling. As the title suggests, the new edition covers more topics on dose and dose adjustment. A large part of the book has been rewritten.

Jixian Wang?is a statistical methodologist in Bristol Myers Squibb, Switzerland. He has worked on drug development for over twenty years and was an academic researcher before joining the pharmaceutical industry. His research interests include statistical methodology and its applications to real problems in pharmaceuticals, including exposure-safety, PKPD modeling, treatment/dose selection, health economics, benefit-risk and health technology assessments, and optimal trial design with 60+ publications on peer-reviewed journals.

This thoroughly revised and updated new edition reflects the progress that has been made in dose-exposure-response (DER) modeling. As the title suggests, the new edition covers more topics on dose and dose adjustment. A large part of the book has been rewritten, including an updated Bayesian analysis and modeling chapter with new materials on ap-proximate Bayesian modeling with misspecified models, Bayesian bootstrap for the cut-the-feedback approach, and meta-regression with Stan codes for implementation. Two new chapters in this edition include one on causal DER modeling, with an introduction to the concept of causal DER relationship, approaches such as the generalized propensity score and instrumental/control function approaches for adjustment for observed and un-observed confounders, and Bayesian causal DER modeling. Another new chapter is dedicated to learning DER relationships with the concept and methods of machine learning, including applications to adaptive dose finding trials by bandits, contextual bandits, and Thompson sampling with Bayesian bootstrap, adaptive control for tracking using a dynamic model with lJ