Bücher der Reihe Chapman & Hall/CRC Interdisciplinary Statistics

The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data. Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

This new edition of a bestseller provides a nontechnical and thoroughly up-to-date review of methods and issues related to clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. The book has been restructured with separate chapters and expanded discussions on general clinical trial issues and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.

Due to recent advances in methodology that offer significant improvements over conventional methods, there is increasing interest in the use of time series models for the study of neuroscience data such as EEG, MEG, fMRI, and NIRS. Written by one of the pioneers of these methods, this book presents an overview of time series models for the study of neuroscience data. It is accessible to applied statisticians working with neuroscience data as well as quantitatively trained neuroscientists. The book is supported by many real examples to illustrate the methods provides computational toolbox on the web, which enables readers to apply the methods to real data.

Addresses statistical challenges posed by inaccurately measuring explanatory variables, a common problem in biostatistics and epidemiology. This book explores both measurement error in continuous variables and misclassification in categorical variables. It is suitable for biostatisticians, epidemiologists, and students.

Develops a methodology that addresses the importance of scientific relevance, biological variability, and invariance of the statistical and scientific inferences with respect to the arbitrary choice of the coordinate system.

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.

This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation.

Capture-recapture methods have recently become popular in the social and medical sciences to estimate the size of elusive populations such as illicit drug users or people with a drinking problem. This book brings together important developments which allow the application of these methods with contributions from more than 40 researchers.

This book fills a gap by covering the principles of data and information essential for every researcher. These topics are firmly planted in existing graduate curricula, and covered in the research methods courses required of most master's level programs, yet a comprehensive and authoritative text did not exist until now.