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UID:20250814T062620EDT-1791sdtjUC@132.216.98.100
DTSTAMP:20250814T102620Z
DESCRIPTION:Quantifying Risk Stratification Provided by Diagnostic Tests an
 d Risk Predictions: Application to Population Mutation Screening\n\nhttp:/
 /dceg.cancer.gov/about/staff-directory/biographies/A-J/katki-hormuzdA prop
 erty of diagnostic tests and risk models deserving more attention is risk 
 stratification\, defined as the ability of a test or model to separate tho
 se at high absolute risk of disease from those at low absolute risk. Risk 
 stratification fills a gap between measures of classification (i.e. AUC) t
 hat do not require absolute risks and decision-analysis that requires not 
 only absolute risks but also subjective specification of costs and utiliti
 es. We introduce Mean Risk Stratification (MRS) as the average change in r
 isk of disease (posttest-pretest) revealed by a diagnostic test or risk mo
 del dichotomized at a risk threshold. MRS is particularly valuable for rar
 e conditions\, where AUC can be high but MRS can be low\, identifying situ
 ations that temper overenthusiasm for screening with the new test/model. W
 e apply MRS to the controversy over who should get testing for mutations i
 n BRCA1/2 that cause high risks of breast and ovarian cancers. To reveal d
 ifferent properties of risk-thresholds to refer women for BRCA1/2 testing\
 , we propose an eclectic approach considering MRS and other metrics. The v
 alue of MRS is to interpret AUC in the context of BRCA1/2 mutation prevale
 nce and to provide a range of risk thresholds at which a risk model is 'op
 timally informative'.\n
DTSTART:20181023T193000Z
DTEND:20181023T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Hormuzd Katki\, PhD\, Senior Investigator - National Cancer Institu
 te
URL:/mathstat/channels/event/hormuzd-katki-phd-senior-
 investigator-national-cancer-institute-290817
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