We are analyzing https://link.springer.com/article/10.1007/s13312-011-0055-4.

Title:
Receiver operating characteristic (ROC) curve for medical researchers | Indian Pediatrics
Description:
Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.
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         headline:Receiver operating characteristic (ROC) curve for medical researchers
         description:Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.
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            Sensitivity
            Specificity
            Receiver operating characteristic curve
            Sample size
            Optimal cut-off point
            Partial area under the curve
            Pediatrics
            Pediatric Surgery
            Maternal and Child Health
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         Specificity
         Receiver operating characteristic curve
         Sample size
         Optimal cut-off point
         Partial area under the curve
         Pediatrics
         Pediatric Surgery
         Maternal and Child Health
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