Introductory Adaptive Trial Designs A Practical Guide with R

All the Essentials to Start Using Adaptive Designs in No Time

Compared to traditional clinical trial designs, adaptive designs often lead to increased success rates in drug development at reduced costs and time. Introductory Adaptive Trial Designs: A Practical Guide with R motivates newcomers to quickly and easily grasp the essence of adaptive designs as well as the foundations of adaptive design methods.

The book reduces the mathematics to a minimum and makes the material as practical as possible. Instead of providing general, black-box commercial software packages, the author includes open-source R functions that enable readers to better understand the algorithms and customize the designs to meet their needs. Readers can run the simulations for all the examples and change the input parameters to see how each input parameter affects the simulation outcomes or design operating characteristics.

Taking a learning-by-doing approach, this tutorial-style book guides readers on planning and executing various types of adaptive designs. It helps them develop the skills to begin using the designs immediately.

Introduction. Classical Design. Two-Stage Adaptive Confirmatory Design Method. K-Stage Adaptive Confirmatory Design Methods. Sample-Size Reestimation Design. Special Two-Stage Group Sequential Trials. Pick-the-Winners Design. The Add-Arms Design. Biomarker-Adaptive Design. Response-Adaptive Randomization. Adaptive Dose-Escalation Trial. Deciding Which Adaptive Design to Use. Monitoring Trial and Making Adaptations. Data Analyses of Adaptive Trials. Planning and Execution. Appendices. Bibliography. Index.

Mark Chang is vice president of biometrics at AMAG Pharmaceuticals and an adjunct professor at Boston University. Dr. Chang is an elected fellow of the American Statistical Association and a co-founder of the International Society for Biopharmaceutical Statistics. He serves on the editorial boards of statistical journals and has published eight books, including Principles of Scientific Methods, Paradoxes in Scientific Inference, Modern Issues and Methods in Biostatistics, Monte Carlo Simulation for the Pharmaceutical Industry, and Adaptive Design Theory and Implementation Using SAS and R, Second Edition.