American Statistical Association Risk Analysis Section Virtual Workshop
An Introduction to Risk Prediction
Ruth Pfeiffer, PhD
Biostatistics Branch, National Cancer Institute
1 PM – 3 PM U.S. Eastern Time, October 23, 2023
Abstract: First we define notions of “risk;” a clinically very relevant quantity is absolute (or “crude”) risk, the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. 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. Methodological issues relevant to the development and evaluation of absolute risk prediction models will be discussed, and various study designs and data for model building will be presented, including cohort, nested case-control, and case-control data combined with population 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 to screen a population and the use of risk models to decide whether to take a preventive intervention that has both beneficial and adverse effects. Various applications will be discussed. If time permits, model updating and model validation with missing predictors will be addressed.
Prerequisites: The course attendees should have a knowledge of basic statistic modeling approaches, epidemiologic study designs, and a foundation in survival analysis.
Presenter Background: Dr. Pfeiffer is a tenured senior investigator in the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI). She received an M.S. degree in applied mathematics from the Technical University of Vienna, Austria, and an M.A. degree in applied statistics and a Ph.D. in mathematical statistics—both from the University of Maryland, College Park. At NCI she is an active collaborator on many research projects and mentors several fellows and junior investigators. Her research focuses on statistical methods for risk prediction, problems arising in molecular and genetic epidemiologic studies, and the analysis of data from electronic medical records. She is the recipient of a Fulbright Fellowship, an elected Member of the International Statistical Institute, and an elected Fellow of the American Statistical Association.
Registration:
· Fee: $25 non-members, $20 members
· Deadline: October 20, 2023
· Registration link is available at https://www.eventbrite.com/e/717813879637
· Contact Yue Jiang at yue.jiang@duke.edu for questions related to the registration process or to receive confirmation.
· Zoom link for registered participants will be sent by October 21, 2023 to the email noted in the registration.
· Registered participants unable to join the live webinar will receive a link to the recorded workshop.