A novel metric that quantifies risk stratification for evaluating diagnostic tests: The example of evaluating cervical-cancer screening tests across populations.

ABSTRACT: Our work involves assessing whether new biomarkers might be useful for cervical-cancer screening across populations with different disease prevalences and biomarker distributions. When comparing across populations, we show that standard diagnostic accuracy statistics (predictive values, risk-differences, Youden's index and Area Under the Curve (AUC)) can easily be misinterpreted. We introduce an intuitively simple statistic for a 2 × 2 table, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS has 3 key advantages for comparing test performance across populations with different disease prevalences and biomarker distributions. First, MRS demonstrates that convent

SUBMITTER: Katki HA 

PROVIDER: S-EPMC5851601 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature