Public Health Sciences Division
Predicting an individual's risk of an outcome is a goal in many areas of medicine today. Examples include future onset of cancer or cardiovascular events in healthy individuals, morbidity/mortality events in patients diagnosed with disease, and response to treatment in patents following treatment for disease. Risk predictions are sought to guide the use of treatment or preventative interventions, to identify high risk subjects who can be recruited into clinical trials of these interventions, or simply to inform individuals of their risk. Risk prediction models are essentially calculators that output a risk estimate given an individual's data on factors such as demographics, clinical characteristics, genetics, and/or the results of imaging or biomarker analyses. Given the growing interest in "personalized medicine" and the rapid development of high throughput technologies, the pursuit of risk prediction models, and of constituent risk predictors, is ever-expanding. The clinical impact of these models is also multiplying, given their frequent dissemination over the internet. Yet there has recently been considerable controversy in the field regarding the ideal evaluation of a risk prediction model.
This symposium included talks illustrating the development, evaluation, and clinical use of risk prediction models; talks summarizing and interpreting the recent controversy regarding model evaluation; and discussions, led by an expert panel, of current "best practices."
We are very grateful to our speakers for making their presentations available to the public. Click on the titles below to view or download.
Polly Newcomb, FHCRC
|What is risk prediction?
Andrew Freedman, NCI
|Illustration of the development and evaluation of a risk prediction model.
Stephanie Kovalchik, RAND
|Issues in developing risk prediction models, for low and high dimensional markers.
Charles Kooperberg, FHCRC
|What is the recent controversy in evaluating risk prediction models all about?
Margaret Pepe, FHCRC
|Net benefit: what really matters when evaluating a risk prediction model.
Carrie Bennette, University of Washington
|The ideal evaluation of a risk prediction model: A randomized trial.
Holly Janes, FHCRC
|Illustration of the evaluation of a risk prediction model in a randomized trial.
Parvin Tajik, University of Amsterdam
|Using a risk prediction model in clinical practice.
Joann Elmore, University of Washington
|Three presentations of risk prediction research by local faculty.
Follow up discussion was led by Katie Kerr, University of Washington.
Thomas Lumley, University of Auckland