Stata commands roccurve and comproc: Accommodating Covariates in ROC Analysis. Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e. the ROC curve), we describe methods for modelling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and ask how much discriminatory accuracy improves with the addition of the marker to the covariates (incremental value). These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Zarnegarnia, Yalda; Messinger, Shari: Review and limitations of methods for constructing a receiver operating characteristic curve in a case-control design (2019)
- White, Ian R.; Rapsomaniki, Eleni: Covariate-adjusted measures of discrimination for survival data (2015)
- Kada, Akiko; Cai, Zhihong; Kuroki, Manabu: Medical diagnostic test based on the potential test result approach: bounds and identification (2013)
- Rodríguez-Álvarez, María Xosé; Tahoces, Pablo G.; Cadarso-Suárez, Carmen; Lado, María José: Comparative study of ROC regression techniques -- applications for the computer-aided diagnostic system in breast cancer detection (2011)
- Li, Caixia; Lu, Ying: Evaluating the improvement in diagnostic utility from adding new predictors (2010)