Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships. Written by an applied statistician with extensive practical experience in drug development, this book explores a wide range of topics in exposure-response modeling, from traditional PKPD modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book emphasizes dose adjustment and treatment adaptation based on dynamic exposure-response models, illustrates how to apply causal inference to exposure-response modeling in pharmacometrics and epidemiology, and links exposure-response modeling to clinical decision making through model-based decision analysis.

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.

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.