This book addresses the development, evaluation, and application of models of absolute risk- the probability of developing a specific disease over a specified time interval in the presence of competing causes of mortality. It discusses the development of appropriate statistical methods for estimating and applying absolute risk.
This book addresses the development, evaluation, and application of models of absolute risk- the probability of developing a specific disease over a specified time interval in the presence of competing causes of mortality. It discusses the development of appropriate statistical methods for estimating and applying absolute risk.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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