Absolute Risk Prediction workshop

Absolute (or "crude") risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease at subsequent age interval. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. This course will define absolute risk and discuss methodological issues relevant to the development and evaluation of risk prediction models. Various study designs and data for model building will be presented, including cohort, nested case-control, and case-control data combined with registry data. Issues relating to the evaluation of risk prediction models and the strengths and limitations of risk prediction models for various applications will be discussed. Standard criteria for model assessment will be presented, as well as loss function-based criteria applied to the use of risk models for screening or preventive interventions.

Course prerequisites: The course attendees should have a knowledge of basic statistics, epidemiologic designs, and a foundation in survival analysis.

Learning objectives: The attendees of the short course will learn what absolute (or "crude") risk is, what it can be used for, how to estimate it from data obtained through various designs, and how to assess the usefulness and validity of a model of absolute risk.

+ show speakers and program
Dr Mitchell H. Gail and Ruth Pfeiffer (US National Cancer Institute)
dr Michael Hauptmann (NKI-AVL)

18 Oct - 18 Oct 2012
meeting website