Key result
Proposed bias-corrected estimator improves multivariate AUC accuracy by minimizing bias and mean square error.
Why the study?
Measurement errors affect summary measures of ROC curves, but few researchers have addressed this issue, and existing work is largely limited to a univariate setup.
A proposed bias-corrected estimator improves the estimation of the area under the multivariate ROC curve in the presence of measurement errors.
May enhance error-adjusted AUC estimation; leaves open validation across clinical datasets before adoption.
Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.
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Siva et al. (2022) studied this question. Bias-corrected estimator for multivariate ROC curve AUC was evaluated on Area under the multivariate ROC curve (AUC) estimation accuracy. A proposed bias-corrected estimator for the area under the multivariate ROC curve in the presence of measurement errors helps in correcting the AUC with minimum bias and minimum mean square error.
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