Online Program Home
My Program

Abstract Details

Activity Number: 449 - Evaluating Risk Predictions for Use in Decision-Making
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #300228 Presentation
Title: Quantifying Risk Stratification Provided by Diagnostic Tests and Risk Predictions
Author(s): Hormuzd Katki*
Companies: US National Cancer Institute
Keywords: prediction; screening; risk modeling; AUC; diagnostic testing

A property of diagnostic tests and risk models deserving more attention is risk stratification, defined as the ability of a test or model to separate those at high absolute risk of disease from those at low absolute risk. Risk stratification fills a gap between measures of classification (i.e. AUC) that do not require absolute risks and decision-analysis that requires not only absolute risks but also subjective specification of costs and utilities. We introduce Mean Risk Stratification (MRS) as the average change in risk of disease (posttest-pretest) revealed by a diagnostic test or risk model dichotomized at a risk threshold. MRS is particularly valuable for rare conditions, where AUC can be high but MRS can be low, identifying situations that temper overenthusiasm for screening with the new test/model. We apply MRS to the controversy over who should get testing for mutations in BRCA1/2 that cause high risks of breast and ovarian cancers. The value of MRS is to interpret AUC in the context of BRCA1/2 mutation prevalence and to provide a range of risk thresholds at which a risk model is "optimally informative".

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program