1st Edition
Exposure-Response Modeling Methods and Practical Implementation
Introduction. Basic exposure and exposure-response models. Dose-exposure and exposure-response models for longitudinal data. Sequential and simultaneous exposure-response modeling. Exposure-risk modeling for time-to-event data. Modeling dynamic exposure-response relationships. Bayesian modeling and model-based decision analysis. Confounding bias and causal inference in exposure-response modeling. Dose-response relationship, dose determination, and adjustment. Implementation using software. Appendix. Bibliography. Index.
Biography
Jixian Wang is a principal statistician at Celgene International, Switzerland. He worked on drug development for 14 years at GSK and Novartis Pharma and was an academic researcher at Edinburgh University and Dundee University, where he is still an honorary research fellow. 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.
"...the book is worth reading as it takes the reader all the way from basic to state-of-the-art exposure-response modeling approaches and challenges. It focuses on detailed mathematical derivations, with many insights based on practical experience. Moreover, many data examples are accompanied by software code ..."
~Biometrical Journal" . . . the greatest strength of this book is that the models and methodologies are always motivated and explained by applications and examples, which effectively communicates to readers the basic ideas behind complex methodologies. Also, practical implementation and computer code are discussed alongside the methods, which will help readers to apply the methods to their own data."
~University of Texas Health Science Center at Houston"In summary, this book provides a good overview of various scenarios of ER relationship assessment and modelling, and the appropriate statistical approaches. With a lot of hints and tips and numeric examples that illustrate various aspects it is easy to read for both statisticians and nonstatisticians; the numerous programming code examples also make the book notably expedient."
~Dirk Lindner, ISCB
