A Review of Methods and Applications of the ROC Curve in Clinical Trials
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A Review of Methods and Applications of the ROC Curve in Clinical Trials Ramesh V Kumar and Grace Maria Antony Drug Information Journal 2010 44: 659 DOI: 10.1177/009286151004400602 The online version of this article can be found at: http://dij.sagepub.com/content/44/6/659
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A Review of Methods and Applications of the ROC Curve in Clinical Trials
Raaesh Kimar V, MSc Associate Biostatistician, Department of Biometrics, Mahocare Clinical Research Limited, Hyderabad. India Grace Maria Antony, PhD Director, Statistics. Department of Biometrics, Makrocare Clinical Research Limited, Hyderabad. India
Key Words ROC curve; Clinical trial: Signal detection: Sensitivity; Specifcity; AUC Carrespoidencr Address Grace Maria Antony (email: grace.antony @rnakrocare. corn), Ramesh Kumar V (email: ramesh.vishwaka@ makrocare.corn).
A receiver operating characteristic cuwe (ROC) is a statistical tod to assess the accuracy of a test. In this article, we propose to review the methods and applications of the R O C cuwe for various situations in clinical trials and drug development. The ROC cuwe is widely accepted as a method for selecting an optimal cutoff point and for comparing the accuracy of diagnostic tests. It is a plot of the true positive rate against the false positive rate for differentcutoff points. The sensitivity and specificity of a
INTRODUCTION A receiver operating characteristic (ROC) curve is a statistical tool to assess the accuracy of test. The ROC curve is part of signal detection theory, which was first used during World War II for the analysis of radar signals. Radar operators had to decide whether a blip on the screen represented an enemy target, a friendly ship, or just noise. Signal detection theory measures the ability of radar receiver operators to make these important distinctions. Their ability to do so is called receiver operating characteristics. The ROC curve is obtained by plotting sensitivity on the y-axis against 1 - specificity on the x-axis (1.2) (as shown in Figure 1).Sensitivity can be defined as positivity in the presence of a disease or condition. It is calculated solely from the affected subgroup. Specificity can be defined as negativity in the absence of disease. It is calculated entirely from the unaffected subgroup. It is a plot of the true positive rate (TPR) against the false positive rate (FPR) for different cutoff points. Each point on the ROC plot represents a sensitivity and specif
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