Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II-III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites. Scott Berry talks about the book on the CRC Press YouTube Channel.

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling

Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application.

New to the Second Edition

• New chapter on spatial point patterns developed primarily from a modeling perspective
• New chapter on big data that shows how the predictive process handles reasonably large datasets
• New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling
• New chapter on the theoretical aspects of geostatistical (point-referenced) modeling
• Greatly expanded chapters on methods for multivariate and spatiotemporal modeling
• New special topics sections on data fusion/assimilation and spatial analysis for data on extremes
• Double the number of exercises
• Many more color figures integrated throughout the text
• Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages

The Only Comprehensive Treatment of the Theory, Methods, and Software

This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling-now commonplace in Bayesian data analysis A new chapter on Bayesian design that emphasizes Bayesian clinical trials A completely revised and expanded section on ranking and histogram estimation A new case study on infectious disease modeling and the 1918 flu epidemic A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem-available both electronically and in print Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.