Absolute Risk: Methods and Applications in Clinical Management and Public Health, 1st Edition (Hardback) book cover

Absolute Risk

Methods and Applications in Clinical Management and Public Health, 1st Edition

By Ruth M. Pfeiffer, Mitchell H. Gail

Chapman and Hall/CRC

201 pages | 50 B/W Illus.

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Hardback: 9781466561656
pub: 2017-07-26
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pub: 2017-08-10
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Description

Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk.

Features:

  • Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression
  • Discusses various sampling designs for estimating absolute risk and criteria to evaluate models
  • Provides details on statistical inference for the various sampling designs
  • Discusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications
  • Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies
  • Discusses model updating, family-based designs, dynamic projections, and other topics

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.

Reviews

"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

Table of Contents

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

About the Authors

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.

About the Series

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED028000
MEDICAL / Epidemiology
TEC010000
TECHNOLOGY & ENGINEERING / Environmental / General