• 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.

• 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.

• 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.

• 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.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6781-6785
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• 2014

Purpose: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End Results (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. Materials and Methods: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A Monte Carlo algorithm was used to estimate the modeling errors. Results: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). Conclusions: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

• Asian Pacific Journal of Cancer Prevention
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• 제15권10호
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• pp.4143-4145
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• 2014

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) Ewing sarcoma (ES) outcome data. The aim of this study was to identify and optimize ES-specific survival prediction models and sources of survival disparities. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ES. 1844 patients diagnosed between 1973-2009 were used for this study. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome (bone and joint specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: The mean follow up time (S.D.) was 74.48 (89.66) months. 36% of the patients were female. The mean (S.D.) age was 18.7 (12) years. The SEER staging has the highest ROC (S.D.) area of 0.616 (0.032) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged) to a simpler non-metastatic (I and II) versus metastatic (III) versus un-staged model. The ROC area (S.D.) of the 3-tiered model was 0.612 (0.008). Several other biologic factors were also predictive of ES-specific survival, but not the socio-economic factors tested here. Conclusions: ROC analysis measured and optimized the performance of ES survival prediction models. Optimized models will provide a more efficient way to stratify patients for clinical trials.

• 한국한의학연구원논문집
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• 제17권2호
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• pp.107-113
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• 2011

Objectives : Sasang typology is extensively studied for the Sasang constitution diagnosis objectification with various data, for example, questionaires, reference materials, etc and analyzed with the several statistical methods. In this study, we used ROC-curve (Receiver Operating Characteristic curve) analysis to diagnose Sasang constitution, which is a kind of epidemiologic research methods and is away from traditional statistical methods. Methods : We collected personality questionnaire which consists of 15 items, from 24 oriental medical clinics. We analyzed the sensitivity and specificity using ROC curve method based on the score of personality questionnaire and also investigated classification accuracy and cut-off value of Sasang constitution. Results : The AUC (area under the ROC curve) value was 0.508 (p=.5511) for Taeeumin, 0.629 (p<.0001) for Soeumin and 0.604(p<.0001) for Soyangin, respectively. so the classification accuracy for Soeumin was highest Soeumin for over 30 points and Soyangin for below 28 points respectively. Conclusions : We suggest that Taeeumin is not classified easily in the ROC-curve analysis. We may classify Soeumin and Soyangin but the accuracy of Sasang constitutional diagnosis is still low.

• 한국물환경학회지
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• 제33권2호
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• pp.130-139
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• 2017

With the need to evaluate accuracy of real-time measurement of cyanobacterial fluorescence to determine cyanobacterial blooms, this research examined 357 paired data (2013-2016) comprising both microscopic toxic cyanobacterial cell counts and concurrent real-time cyanobacterial concentrations at 2 sites (YS1 and YS2) in Yeongsan river. The increase in real-time cyanobacterial concentration was closely associated with the exceedance of 5,000 cyanobacterial cells/ml (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.03-1.12) and 10,000 cells/ml (OR 1.08, 95% CI 1.04-1.12) at YS2 site. The area under the receiver operating characteristic (ROC) curve for the real-time cyanobacterial measurement at the YS2 site was 0.93, which indicates the measurement provides a high accurate detection of cyanobacterial blooms. On the ROC curve, the early alert levels of real-time cyanobacteria ranging $16-23{\mu}g$ chl-a/L would produce acceptable sensitivity of 79% and specificities greater than 90%. The real-time fluorescence measurement was found to be an accurate indicator of cyanobacteria and can serve as a tool for detecting toxic cyanobacterial bloom events in Youngsan river.

• Asian Pacific Journal of Cancer Prevention
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• 제17권1호
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• pp.347-352
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• 2016

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. Conclusions: Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.