Pfeiffer / Gail Absolute Risk

Methods and Applications in Clinical Management and Public Health
1. Auflage 2017
ISBN: 978-1-4665-6168-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 225 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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.

Zielgruppe

Researchers, practitioners, and graduate students in biostatistics, epidemiology, and medicine.

Autoren/Hrsg.

Pfeiffer, Ruth M.

Gail, Mitchell H.

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Mathematik | Informatik Mathematik Stochastik
• Geowissenschaften Umweltwissenschaften Umwelttechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

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

Über Autor(innen)

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.