Med Decis Making - Lehmann family of ROC curves.

Tópicos

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Resumo

Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve and the sensitivity at a given level of specificity) have simple analytic forms. Closed-form expressions for the functional estimates and their corresponding asymptotic variances are derived. This family accommodates the comparison of multiple markers, covariate adjustments, and clustered data through a regression formulation. Evaluation of the underlying assumptions, model fitting, and model selection can be performed using any off-the-shelf proportional hazards statistical software package.

Resumo Limpo

receiv oper characterist roc curv evalu discriminatori power continu marker predict binari outcom popular parametr model roc curv binorm model assum marker monoton transform normal distribut condit outcom author present altern binorm model base lehmann famili also known proport hazard specif result roc curv function area curv sensit given level specif simpl analyt form closedform express function estim correspond asymptot varianc deriv famili accommod comparison multipl marker covari adjust cluster data regress formul evalu under assumpt model fit model select can perform use offtheshelf proport hazard statist softwar packag

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