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Introduction. Definitions and Basic Concepts for Survival Data in a Cohort without Covariates. Developing Absolute Risk Models from Cohort Data with Covariates. Estimating Absolute Risk from Case-Cohort and Nested Case-Control Data. Estimating Absolute Risk from Population-Based Case-Control and Registry Data. Evaluation of Adequacy of Model. Comparing Two Models. Special Topic: Disease Prognosis. Special Topic: Family-Based Designs
Ruth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association, with interests in risk modeling, dimension reduction, and applications in epidemiology. She developed absolute risk models for breast cancer, colon cancer, melanoma, and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used "Gail model" for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health.
"Written by two leading experts in the field, this book provides a comprehensive overview of absolute risk, including both theoretical basis and clinical implications before and after the disease diagnosis. Equipped with sufficient technical details on the estimation and inference of absolute risk aswell as a range of real examples, this book is targeted toward a broad audience, including epidemiologists, clinicians, and statisticians. While a few other books on theoretical aspects of absolute risk are available in the literature, the book by Pfeiffer and Gail treats absolute risk from several new angles . . ." ~Journal of the American Statistical Association "This book provides an excellent comprehensive basis for researchers or advanced courses devoted to the development and assessment of absolute risk models. Ruth Pfeiffer and Mitchell Gail have a long history of active and successful research in the field of risk prediction modeling, the first publication of what has become known as the Gail-Model for breast cancer risk prediction having appeared over 25 years ago. This background allows them to present a broad overview of various model situations and modeling approaches together with various real-life data examples. It is a pleasure to see that assumptions and inference are treated with mathematical stringency in all addressed topics. The mathematical framework is introduced, motivated, and translated into a clinically meaningful context using worked examples, so as to give access to mathematically less experienced readers. ~Biometric Journal