Determination of Optimum Cutoff Levels of Plasma Des-γ-Carboxy Prothrombin and Serum α-Fetoprotein for the Diagnosis of Hepatocellular Carcinoma using Receiver Operating Characteristic Curves

Tumor Biology ◽  
1992 ◽  
Vol 13 (5-6) ◽  
pp. 316-323 ◽  
Author(s):  
Shigetoshi Fujiyama ◽  
Kiyonori Izuno ◽  
Kunio Yamasaki ◽  
Tatsuo Sato ◽  
Kazuhisa Taketa
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Alpha Fetoprotein ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Serum Alpha Fetoprotein ◽  
Cutoff Levels ◽  
Receiver Operating

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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