Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models
Receiver-operating characteristic (ROC) analysis was originally developed during World War II to analyze classification accuracy in differentiating signal from noise in radar detection.1 Recently, the methodology has been adapted to several clinical areas heavily dependent on screening and diagnostic tests,2–4 in particular, laboratory testing,5 epidemiology,6 radiology,7–9 and bioinformatics.10
ROC analysis is a useful tool for evaluating the performance of diagnostic tests and more generally for evaluating the accuracy of a statistical model (eg, logistic regression, linear discriminant analysis) that classifies subjects into 1 of 2 categories, diseased or nondiseased. Its function as a simple graphical tool for displaying the accuracy of a medical diagnostic test is one of the most well-known applications of ROC curve analysis. In Circulation from January 1, 1995, through December 5, 2005, 309 articles were published with the key phrase “receiver operating characteristic.” In cardiology, diagnostic testing plays a fundamental role in clinical practice (eg, serum markers of myocardial necrosis, cardiac imaging tests). Predictive modeling to estimate expected outcomes such as mortality or adverse cardiac events based on patient risk characteristics also is common in cardiovascular research. ROC analysis is a useful tool in both of these situations.
In this article, we begin by reviewing the measures of accuracy—sensitivity, specificity, and area under the curve (AUC)—that use the ROC curve. We also illustrate how these measures can be applied using the evaluation of a hypothetical new diagnostic test as an example.
Diagnostic Test and Predictive Model
A diagnostic classification test typically yields binary, ordinal, or continuous outcomes. The simplest type, binary outcomes, arises from a screening test indicating whether the patient is nondiseased (Dx=0) or diseased (Dx=1). The screening test indicates whether the patient is likely to be diseased or not. When >2 categories are used, the test data can be on an ordinal rating scale; eg, echocardiographic grading of mitral regurgitation uses a 5-point ordinal (0, 1+, 2+, 3+, 4+) scale for disease severity. When a particular cutoff level or threshold is of particular interest, an ordinal scale may be dichotomized (eg, mitral regurgitation ≤2+ and >2+), in which case methods for binary outcomes can be used.7 Test data such as serum markers (brain natriuretic peptide11) or physiological markers (coronary lumen diameter,12 peak oxygen consumption13) also may be acquired on a continuous scale.
To estimate classification accuracy using standard ROC methods, the disease status for each patient is measured without error. The true disease status often is referred to as the gold standard. The gold standard may be available from clinical follow-up, surgical verification, and autopsy; in some cases, it is adjudicated by a committee of experts.
In selection of the gold standard, 2 potential problems arise: verification bias and measurement error. Verification bias results when the accuracy of a test is evaluated only among those with known disease status.14–16 Measurement error may result when a true gold standard is absent or an imperfect standard is used for comparison.17,18
Sensitivity and Specificity
The fundamental measures of diagnostic accuracy are sensitivity (ie, true positive rate) and specificity (ie, true negative rate). For now, suppose the outcome of a medical test results in a continuous-scale measurement. Let t be a threshold (sometimes called a cutoff) value of the diagnostic test used to classify subjects. Assume that subjects with diagnostic test values less than or equal to t are classified as nondiseased and that subjects with diagnostic test values greater than t are classified as diseased, and let m and n denote the number of subjects in each group. Once the gold standard for each subject is determined, a 2×2 contingency table containing the counts of the 4 combinations of classification and true disease status may be formed; the cells consist of the number of true negatives, false negatives, false positives, and true positives (the Table).
|Nondiseased (GS=0)||Diseased (GS=1)|
|GS indicates gold standard; DX, diagnostic test. Specificity=true negative rate=A/(A+C). Sensitivity=true positive rate=D/(B+D). Negative predictive value=A/(A+B). Positive predictive value=D/(C+D). Disease prevalence=(B+D)/(A+B+C+D).|
|Negative (Dx=0)||A=true negatives||B=false negatives||A+B=test negatives|
|Positive (Dx=1)||C=false positives||D=true positives||C+D=test positives|
|Total||A+C=nondiseased||B+D=diseased||A+B+C+D=total sample size|
The accuracy of such binary-valued diagnostic tests is assessed in terms of the probability that the test correctly classifies a nondiseased subject as negative, namely the specificity (also known as the true negative rate), and the probability that the test correctly classifies a diseased subject as positive, namely the sensitivity (also known as the true positive rate) (Figure 1).
When evaluating a continuous-scale diagnostic test, we need to account for the changes of specificity and sensitivity when the test threshold t varies. One may wish to report the sum of sensitivity and specificity at the optimal threshold (discussed later in greater detail). However, because the optimal value of t may not be relevant to a particular application, it can be helpful to plot sensitivity and specificity over a range of values of interest, as is done with an ROC curve. This inherent tradeoff between sensitivity and specificity also can be demonstrated by varying the choice of threshold.
An ROC curve is a plot of sensitivity on the y axis against (1−specificity) on the x axis for varying values of the threshold t. The 45° diagonal line connecting (0,0) to (1,1) is the ROC curve corresponding to random chance. The ROC curve for the gold standard is the line connecting (0,0) to (0,1) and (0,1) to (1,1). Generally, ROC curves lie between these 2 extremes. The area under the ROC curve is a summary measure that essentially averages diagnostic accuracy across the spectrum of test values Figure 2).
The empirical method for creating an ROC plot involves plotting pairs of sensitivity versus (1−specificity) at all possible values for the decision threshold when sensitivity and specificity are calculated nonparametrically. An advantage of this method is that no structural assumptions are made about the form of the plot, and the underlying distributions of the outcomes for the 2 groups do not need to be specified.19 However, the empirical ROC curve is not smooth (Figure 3). When the true ROC curve is a smooth function, the precision of statistical inferences based on the empirical ROC curve is reduced relative to a model-based estimator (at least when the model is correctly specified). Analogous to regression, the specification of a model for the ROC curve enables information to be pooled over all values when estimating sensitivity or specificity at any 1 point. Smooth nonparametric ROC curves may be derived from estimates of density or distribution functions of the test distributions.20
As an alternative to the nonparametric approach, parametric models such as the binormal model may be assumed (Figure 3).21–25 The binormal model assumes that both measurements have 2 independent normal distributions with different means and SDs. In our example, the distributions have a mean of 0 and an SD of 1 for the nondiseased population and a mean of 1.87 and an SD of 1.5 for the diseased population. These models have the further advantage of allowing easy incorporation of covariates into the model. By incorporating an optimal transformation, typically a log transformation to normal distributions, the estimated ROC curve may yield a better fit.26–28
A 95% confidence interval for the sensitivity at a given specificity, or vice versa, may be constructed using the bootstrap29,30 or, for a bayesian model, using Markov-chain Monte Carlo simulation.31 Alternatively, sample analytical approximations may be used instead of these computationally intensive numerical procedures.
Area Under the Curve
The AUC is an overall summary of diagnostic accuracy. AUC equals 0.5 when the ROC curve corresponds to random chance and 1.0 for perfect accuracy. On rare occasions, the estimated AUC is <0.5, indicating that the test does worse than chance.31
For continuous diagnostic data, the nonparametric estimate of AUC is the Wilcoxon rank-sum test, namely the proportion of all possible pairs of nondiseased and diseased test subjects for which the diseased result is higher than the nondiseased one plus half the proportion of ties. Under the binormal model, the AUC is a simple function of the mean and variance.21,32
Comparison of AUC Curves
An important problem concerns the comparison of 2 AUCs derived from 2 diagnostic tests administered on the same set of patients. Correlated U statistics may be compared.33 Pearson correlation coefficients were used to estimate the correlation of the 2 AUCs.34 A family of nonparametric comparisons based on a weighted average of sensitivities may be conducted.35
The area under the ROC curve is a simple and convenient overall measure of diagnostic test accuracy. However, it gives equal weight to the full range of threshold values. When the ROC curves intersect, the AUC may obscure the fact that 1 test does better for 1 part of the scale (possibly for certain types of patients) whereas the other test does better over the remainder of the scale.32,36 The partial area may be useful for the range of specificity (or sensitivity) of clinical importance (ie, between 90% and 100% specificity). However, partial area may be more difficult to estimate and compare on the basis of numerical integration methods; thus, full area is used more frequently in practice.37
One criterion for evaluating the optimal threshold of a test is to maximize the sum of sensitivity and specificity. This is equivalent to maximizing the difference between the sensitivity of the test and the sensitivity that the test would have if it did no better than random chance.9 For example, if both sensitivity and specificity are of importance in our example binormal model, the optimal threshold of t would be 0.75, where these 2 accuracy measures equal sensitivity and specificity equal 0.77 (Figure 3).
ROC analysis is a valuable tool to evaluate diagnostic tests and predictive models. It may be used to assess accuracy quantitatively or to compare accuracy between tests or predictive models. In clinical practice, continuous measures are frequently converted to dichotomous tests. ROC analysis can be used to select the optimal threshold under a variety of clinical circumstances, balancing the inherent tradeoffs that exist between sensitivity and sensitivity. Several other specific applications of ROC analysis such as sample size determination38–42 and meta-analysis43,44 have been applied to clinical research. These can be derived from the fundamental principles discussed here.
We thank our colleagues, Daniel Goldberg-Zimring, PhD, and Marianna Jakab, MSc, of Brigham and Women’s Hospital, Harvard Medical School, who assist in maintaining a comprehensive literature search website containing articles related to ROC methodology (http://splweb.bwh.harvard.edu:8000/pages/ppl/zou/roc.html).
Sources of Funding
This research was made possible in part by grants R01LM007861, R01GM074068, U41RR019703, and P41RR13218 from the National Institutes of Health (NIH), Bethesda, Md. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
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