2022
DOI: 10.1002/bimj.202000382
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Time‐dependent ROC curve estimation for interval‐censored data

Abstract: The receiver‐operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time‐to‐event studies, the subject's event status is time‐dependent, and hence, time‐dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right‐censoring … Show more

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Cited by 13 publications
(8 citation statements)
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References 31 publications
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“…The model discriminatory performance was measured using the time-dependent receiver operating characteristic (ROC) curve and Harrell concordance index (C-index) (27,28). The ROC curve is a statistical tool used to evaluate the discriminative capacity of a diagnostic test.…”
Section: Discussionmentioning
confidence: 99%
“…The model discriminatory performance was measured using the time-dependent receiver operating characteristic (ROC) curve and Harrell concordance index (C-index) (27,28). The ROC curve is a statistical tool used to evaluate the discriminative capacity of a diagnostic test.…”
Section: Discussionmentioning
confidence: 99%
“…This empirical time-dependent ROC estimator can be smoothed by replacing the indicator function I false( . false) in equation (12) with K b false( . false), where b is a smoothing parameter and K false( x false) = 0 1 k false( s false) d s is a kernel distribution function with a density k. As demonstrated in many studies, see for example, 8,12,13,30 the smooth ROC curve estimators tend to exhibit smaller mean integrated squared errors MISEs as compared to the empirical estimators. Following the smoothing ROC estimator of the uncorrelated (classical) right-censored time-to-event data introduced by Beyene and El Ghouch, 12 the empirical time-dependent ROC curve estimator in equation (12) can be smoothed as ROC ^ t , b false( u false) = n 1 j scriptW ^ t j K false( false( Q false( u false) Q false( Z ^ t j false) false) / b false), where b is a smoothing parameter and Q is a quantile transformation function introduced to overcome the boundary problem that arise due to the fact that …”
Section: Methodsmentioning
confidence: 99%
“…in equation ( 12) with K b (. ), where b is a smoothing parameter and K(x) = ∫ 1 0 k(s)ds is a kernel distribution function with a density k. As demonstrated in many studies, see for example, 8,12,13,30 the smooth ROC curve estimators tend to exhibit smaller mean integrated squared errors MISEs as compared to the empirical estimators. Following the smoothing ROC estimator of the uncorrelated (classical) right-censored time-to-event data introduced by Beyene and El Ghouch, 12 the empirical time-dependent ROC curve estimator in equation ( 12) can be smoothed as…”
Section: Estimatorsmentioning
confidence: 99%
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“…The study showed that circITGA7 and ITGA7 were low expressed in colorectal cancer tissues. The receiver operating characteristic (ROC) curve analysis, which is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker [ 152 154 ], showed that the area under the curve (AUC) of circITGA7 was 0.8791 with a sensitivity (true-positive rate = true positives/[true positives + false negatives]) of 0.9275 and a specificity (true-negative rate = true negatives/[true negatives + false positives]) of 0.6667, which was much higher than that of ITGA7 (AUC = 0.7402) [ 18 ]. AUC (takes values from 0 to 1) is an effective way to summarize the overall diagnostic accuracy of the test.…”
Section: Combination Of Circrnas and Their Host Genes Is A Potential ...mentioning
confidence: 99%