Absolute Risk: Methods and Applications in Clinical Management and Public Health

Ruth M. Pfeiffer, Mitchell H. Gail

July 26, 2017 by Chapman and Hall/CRC
Reference - 201 Pages - 50 B/W Illustrations
ISBN 9781466561656 - CAT# K15946
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability


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  • Describes models for predicting the likelihood of developing cardiovascular disease or breast cancer, which helps patient and their physicians decide whether to take agents such as statins for preventing cardiovascular disease or tamoxifen for preventing breast cancer
  • Shows how absolute risk models are also useful for managing patients after a disease develops because the prognosis can help determine whether onerous interventions are justified
  • Helps readers understand how to estimate absolute risk from various types of samples, ranging from complete cohort data to nested case-control data to population-based case-control data supplemented by population registry data
  • Covers standard methods for assessing absolute risk models as well as new approaches based on expected losses in medical applications


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.


  • 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.