• 한국임상수의학회지
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• 제33권2호
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• pp.97-101
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• 2016

In the field of clinical medicine, diagnostic accuracy studies refer to the degree of agreement between the index test and the reference standard for the discriminatory ability to identify a target disorder of interest in a patient. The receiver operating characteristic (ROC) curve offers a graphical display the trade-off between sensitivity and specificity at each cutoff for a diagnostic test and is useful in assigning the best cutoff for clinical use. In this end, the ROC curve analysis is a useful tool for estimating and comparing the accuracy of competing diagnostic tests. This paper reviews briefly the measures of diagnostic accuracy such as sensitivity, specificity, and area under the ROC curve (AUC) that is a summary measure for diagnostic accuracy across the spectrum of test results. In addition, the methods of creating an ROC curve in single diagnostic test with five-category discrete scale for disease classification from healthy individuals, meaningful interpretation of the AUC, and the applications of ROC methodology in clinical medicine to determine the optimal cutoff values have been discussed using a hypothetical example as an illustration.

• 대한수학회보
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• 제48권3호
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• pp.523-537
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• 2011

A receiver operating characteristic (ROC) curve plots the true positive rate of a classier against its false positive rate, both of which are accuracy measures of the classier. The ROC curve has several interesting geometrical properties, including concavity which is a necessary condition for a classier to be optimal. In this paper, we study the nonparametric maximum likelihood estimator (NPMLE) of a concave ROC curve and its modification to reduce bias. We characterize the NPMLE as a solution to a geometric programming, a special type of a mathematical optimization problem. We find that the NPMLE is close to the convex hull of the empirical ROC curve and, thus, has smaller variance but positive bias at a given false positive rate. To reduce the bias, we propose a modification of the NPMLE which minimizes the $L_1$ distance from the empirical ROC curve. We numerically compare the finite sample performance of three estimators, the empirical ROC curve, the NMPLE, and the modified NPMLE. Finally, we apply the estimators to estimating the optimal ROC curve of the variance-threshold classier to segment a low depth of field image and to finding a diagnostic tool with multiple tests for detection of hemophilia A carrier.

• 응용통계연구
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• 제35권1호
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• pp.35-47
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• 2022

Receiver operating characteristic (ROC) 곡선은 이항 반응 자료에 대한 마커의 분류 예측력을 측정하기 위해 널리 적용되어왔으며 최근에는 생존 분석에서도 매우 중요한 역할을 하고 있다. 여러 가지 유형의 중도 절단과 원인 불명 등 다양한 종류의 결측 자료를 포함한 생존 자료 분석에서 마커의 사건 발생 여부에 대한 예측력을 판단하기 위해 기존의 통계량을 확장하였다. 생존 분석 자료는 각 시점에서의 사건 발생 여부로 이해할 수 있으며, 따라서 시점마다 ROC 곡선과 AUC를 구할 수 있다. 본 논문에서는 우중도 절단과 경쟁 위험 모형하에서 사용되는 다양한 방법론과 관련 R 패키지를 소개하고 각 방법의 특성을 설명하고 비교하였으며 이를 검토하기 위해 간단한 모의실험을 시행하였다. 또한, 프랑스에서 수집된 치매 자료의 마커 분석을 시행하였다.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.911-920
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• 2004

We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• 한국임상수의학회지
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• 제19권3호
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• pp.312-315
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• 2002

질병에 이환된 개체로부터 이환되지 않은 개체를 구분하기 위해 사용되는 대부분의 진단검사는 판별의 기준점 (cut-off value)을 필요로 한다. ROC (receiver operating characteristic) 곡선은 이러한 목적으로 흔히 사용되고 있으며 진단의 기준점을 다양하게 변화시킬 때 진단검사의 정확도 (민감도와 특이도)를 제시해주는 지표로 활용되고 있다. 저자들은 수의학관련 연구자들이 이 방법을 효과적으로 사용할 수 있도록 EXCEL에 내장된 비쥬얼 베이직으로 binormal ROC 곡선의 최대우도비를 계산해주는 프로그램을 작성하였다. 방사선 분야의 자료와 미생물학 자료를 예제로 들어 이 프로그램의 활용성을 높이고자 하였고 이 분야에 관심이 있는 연구자는 저자에게 연락하여 이 프로그램을 얻을 수 있다.

• 응용통계연구
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• 제33권5호
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• pp.541-553
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• 2020

Receiver operating characteristic (ROC) 곡선은 민감도와 특이도로 표현되며, ROC 곡선을 이용하는 최적분류점도 민감도와 특이도만을 반영하지만, 본 연구에서는 질병률과 효용을 추가하여 고려하는 기대효용함수를 연구한다. 특히 교차하는 ROC 곡선들의 area under the ROC curve (AUC) 값들이 유사한 경우에 특정한 부분의 부분 AUC를 비교해야 한다. 본 연구에서는 정의된 민감도 직선과 특이도 직선을 바탕으로 각각 높은 민감도와 특이도를 나타내는 부분 AUC를 제안한다. ROC 곡선들이 교차하고 동일한 AUC 값을 갖는 다양한 분포함수를 설정하여, 민감도 직선과 특이도 직선을 이용하여 구한 부분 AUC를 비교하면서 모형의 판별력을 향상시키는 방법을 제안한다.

• 분석과학
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• 제34권2호
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• pp.78-86
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• 2021

The objective of this study was to investigate urinary cut-off concentrations of quetiapine and risperidone for distinction between normal and abnormal/non-takers who were being placed on probation. Liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was employed for determination of antipsychotic drugs in urine from mentally disordered probationers. The optimal cut-off values of antipsychotic drugs were calculated using receiver operating characteristic (ROC) curve analysis. The sensitivity and specificity of the method for the detection of antipsychotic drugs in urine were subsequently evaluated. The area under the ROC curve (AUC) was 0.927 for norquetiapine and 0.791 for 9-hydroxyrisperidone, respectively. These antipsychotic drugs are classified readily in the ROC curve analysis. The cut-off values for distinguishing regular and irregular/non-takers were 39.1 ng/mL for norquetiapine and 67.9 ng/mL for 9-hydroxyrisperidone, respectively. The results of this study suggest the cut-off values of quetiapine and risperidone were highly useful to distinguish regular takers from irregular/non-takers.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 1994년도 교수 연수회(역학)
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• pp.150-153
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• 1994

Receiver operating characteristic (ROC) curves have been frequently used to compare probability models applied to medical problems. Though the curves are a measure of the discriminatory power of a model. they do not reflect the model's accuracy. A supplementary accuracy curve is derived which will be coincident with the ROC curve if the model is reliable. will be above the ROC curve if the model's probabilities are too high or below if they are too low. A clinical example of this new graphical presentation is given.