Online Program Home
My Program

Abstract Details

Activity Number: 643
Type: Topic Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #321025
Title: A Framework for Quantifying Risk Stratification from Diagnostic Tests
Author(s): Hormuzd Katki*
Companies: National Cancer Institute
Keywords: ROC ; AUC ; Youden's Index ; prediction ; absolute risk

A key property to assess for a new diagnostic test is risk stratification: how well a test separates those at high risk of disease from those at low risk. We introduce the risk stratification distribution: the distribution of the changes in disease risk revealed by each test result. The mean risk stratification (MRS) is the average amount of extra disease that a test reveals for an individual patient. The MRS demonstrates that (1) big risk differences do not imply good risk stratification for markers that are rarely positive, (2) a large Youden's index, or AUC, do not imply good risk stratification if disease is too rare, and (3) risk stratification for rare diseases depends on neither sensitivity nor specificity, but on the difference of specificity and marker negativity. We provide decision-theoretic justification for MRS by demonstrating that the increase in expected benefit over the expected benefit of a random test is proportional to the MRS. We apply this framework to our experience incorporating HPV testing into cervical cancer screening guidelines.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association