Accommodating covariates roc analysis


Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discriminatory accuracy improves (in incremental value) with the addition of the marker to the covariates.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.AU - Pepe, Margaret Sullivan AU - Barlow, William E. Statistical evaluation of medical imaging tests used for diagnostic and prognostic purposes often employs receiver operating characteristic (ROC) curves. The ordinal regression method is the standard approach used when evaluating tests with ordinal values.



For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates.This paper applies receiver operating characteristic (ROC) analysis to micro-level, monthly time series from the M3-Competition.Forecasts from competing methods were used in binary decision rules to forecast exceptionally large declines in demand.Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire.

The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend.

Journal - very nice intro, nonmath, but insightful what has to be done...., includes explanation on PARAMETRIC vs non PARAMETRIC ROC estimation (smooth vs empirical), cf concept of partial AUC ROC--- give nice and simple overview on assumptions, and when PARAMETRIC vs NONparametric methods should be used.



Accommodating covariates roc analysis comments


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