A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data

2016 ◽  
Vol 27 (8) ◽  
pp. 2264-2278 ◽  
Author(s):  
Liang Li ◽  
Tom Greene ◽  
Bo Hu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Area Under The Curve ◽  
Time Dependent ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The time-dependent receiver operating characteristic curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur at different times during the follow-up and hence may be right censored. Due to right censoring, the true disease onset status prior to the pre-specified time horizon may be unknown for some patients, which causes difficulty in calculating the time-dependent sensitivity and specificity. We propose to estimate the time-dependent sensitivity and specificity by weighting the censored data by the conditional probability of disease onset prior to the time horizon given the biomarker, the observed time to event, and the censoring indicator, with the weights calculated nonparametrically through a kernel regression on time to event. With this nonparametric weighting adjustment, we derive a novel, closed-form formula to calculate the area under the time-dependent receiver operating characteristic curve. We demonstrate through numerical study and theoretical arguments that the proposed method is insensitive to misspecification of the kernel bandwidth, produces unbiased and efficient estimators of time-dependent sensitivity and specificity, the area under the curve, and other estimands from the receiver operating characteristic curve, and outperforms several other published methods currently implemented in R packages.

scholarly journals Comparative Analysis of Anterior Segment Parameters in Normal and Keratoconus Eyes Generated by Scheimpflug Tomography

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Faik Orucoglu ◽  
Ebru Toker
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Anterior Segment ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Purpose. To assess and compare the anterior and posterior corneal surface parameters, keratoconus indices, thickness profile data, and data from enhanced elevation maps of keratoconic and normal corneas with the Pentacam Scheimpflug corneal tomography and to determine the sensitivity and specificity of these parameters in discriminating keratoconus from normal eyes.Methods. The study included 656 keratoconus eyes and 515 healthy eyes with a mean age of30.95±9.25and32.90±14.78years, respectively. Forty parameters obtained from the Pentacam tomography were assessed by the receiver operating characteristic curve analysis for their efficiency.Results. Receiver operating characteristic curve analyses showed excellent predictive accuracy (area under the curve, ranging from 0.914 to 0.972) for 21 of the 40 parameters evaluated. Among all parameters indices of vertical asymmetry, keratoconus index, front elevation at thinnest location, back elevation at thinnest location, Ambrósio Relational Thickness (ARTmax), deviation of average pachymetric progression, deviation of ARTmax, and total deviation showed excellent (>90%) sensitivity and specificity in addition to excellent area under the receiver operating characteristic curve (AUROC).Conclusions. Parameters derived from the topometric and Belin-Ambrósio enhanced ectasia display maps very effectively discriminate keratoconus from normal corneas with excellent sensitivity and specificity.

scholarly journals Adjustment for the measurement error in evaluating biomarker performances at baseline for future survival outcomes: Time-dependent receiver operating characteristic curve within a joint modelling framework

2020 ◽  
pp. 263208432097225
Author(s):  
Ruwanthi Kolamunnage-Dona ◽  
Adina Najwa Kamarudin
Keyword(s):  
Measurement Error ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Time Dependent ◽  
Survival Outcomes ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The performance of a biomarker is defined by how well the biomarker is capable to distinguish between healthy and diseased individuals. This assessment is usually based on the baseline value of the biomarker; the value at the earliest time point of the patient follow-up, and quantified by ROC (receiver operating characteristic) curve analysis. However, the observed baseline value is often subjected to measurement error due to imperfect laboratory conditions and limited machine precision. Failing to adjust for measurement error may underestimate the true performance of the biomarker, and in a direct comparison, useful biomarkers could be overlooked. We develop a novel approach to account for measurement error when calculating the performance of the baseline biomarker value for future survival outcomes. We adopt a joint longitudinal and survival data modelling formulation and use the available longitudinally repeated values of the biomarker to make adjustment of the measurement error in time-dependent ROC curve analysis. Our simulation study shows that the proposed measurement error-adjusted estimator is more efficient for evaluating the performance of the biomarker than estimators ignoring the measurement error. The proposed method is illustrated using Mayo Clinic primary biliary cirrhosis (PBC) study.

Optimal combination of biomarkers for time-dependent receiver operating characteristic estimation and related problems

2014 ◽  
Vol 26 (2) ◽  
pp. 898-913
Author(s):  
Zhong Guan ◽  
Jing Qin
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Time Dependent ◽  
Likelihood Method ◽  
Test Accuracy ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The receiver operating characteristic curve is commonly used for assessing diagnostic test accuracy and for discriminatory ability of a medical diagnostic test in distinguishing between diseases and non-diseased individuals. With the advance of technology, many genetic variables and biomarker variables are easily collected. The most challenging problem is how to combine clinical, genetic, and biomarker variables together to predict disease status. If one is interested in predicting t-year survival, however, the status of “case” (death) and “control” (survival) at the given t-year is unknown for those individuals who were censored before t-year. To conduct a receiver operating characteristic analysis, one has to impute those ambiguous statuses. In this paper, we study a maximum pseudo likelihood method to estimate the underlying parameters and baseline distribution functions. The proposed approach produces more efficient and smoother estimate of the optimal time-dependent receiver operating characteristic curve and more stable estimation of the prediction rule for the t-year survivors. More importantly, the proposal is equipped with a goodness-of-fit test for the model assumption based on the bootstrap method. Two real medical data sets are used for illustration.

scholarly journals COMPARISION OF BINARY DIAGNOSTIC PREDICTORS USING ENTROPY

2018 ◽  
Vol 6 (1) ◽  
pp. 440-447
Author(s):  
Kathare Alfred ◽  
Otieno Argwings ◽  
Kimeli Victor
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Gold Standard ◽  
Large Scale ◽  
Operating Characteristic ◽  
Standard Procedure ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The use of gold standard procedures in screening may be costly, risky or even unethical. It is, therefore, not admissible for large scale application. In this case, a more acceptable diagnostic predictor is applied to a sample of subjects alongside a gold standard procedure. The performance of the predictor is then evaluated using Receiver Operating Characteristic curve. The area under the curve, then, provides a summative measure of the performance of the predictor. The Receiver Operating Characteristic curve is a trade-off between sensitivity and specificity which in most cases are of different clinical significance. Also, the area under the curve is criticized for lack of coherent interpretation. In this study, we proposed the use of entropy as a summary index measure of uncertainty to compare diagnostic predictors. Noting that a diseased subject who is truly identified with the disease at a lower cut-off will also be identified at a higher cut-off, we substituted time variable in survival analysis for cut-offs in a binary predictor. We then derived the entropy of the functions of diagnostic predictors. Application of the procedure to real data showed that entropy was a strong measure for quantifying the amount of uncertainty engulfed in a set of cut-offs of binary diagnostic predictor.

scholarly journals Smooth time-dependent receiver operating characteristic curve estimators

2017 ◽  
Vol 27 (3) ◽  
pp. 651-674 ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Juan Carlos Pardo-Fernández
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Time Dependent ◽  
Density Estimator ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The receiver operating characteristic curve is a popular graphical method often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic receiver operating characteristic curve and the incident/dynamic receiver operating characteristic curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. In this article, the authors explore the use of a bivariate kernel density estimator which accounts for censored observations in the sample and produces smooth estimators of the time-dependent receiver operating characteristic curves. The performance of the resulting cumulative/dynamic and incident/dynamic receiver operating characteristic curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. Finally, two real-applications are considered. An R package is also provided as a complement to this article.

scholarly journals Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Demilade Adedinsewo ◽  
Rickey E. Carter ◽  
Zachi Attia ◽  
Patrick Johnson ◽  
Anthony H. Kashou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Left Ventricular Systolic Dysfunction ◽  
Characteristic Curve ◽  
Left Ventricular ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals Prediction of Advisability of Returning Home Using the Home Care Score

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Akiyoshi Matsugi ◽  
Keisuke Tani ◽  
Yoshiki Tamaru ◽  
Nami Yoshioka ◽  
Akira Yamashita ◽  
...  
Keyword(s):  
Home Care ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Functional Independence Measure ◽  
Functional Independence ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Purpose. The aim of this study was to assess whether the home care score (HCS), which was developed by the Ministry of Health and Welfare in Japan in 1992, is useful for the prediction of advisability of home care.Methods. Subjects living at home and in assisted-living facilities were analyzed. Binominal logistic regression analyses, using age, sex, the functional independence measure score, and the HCS, along with receiver operating characteristic curve analyses, were conducted.Findings/Conclusions. Only HCS was selected for the regression equation. Receiver operating characteristic curve analysis revealed that the area under the curve (0.9), sensitivity (0.82), specificity (0.83), and positive predictive value (0.84) for HCS were higher than those for the functional independence measure, indicating that the HCS is a powerful predictor for advisability of home care.Clinical Relevance. Comprehensive measurements of the condition of provided care and the activities of daily living of the subjects, which are included in the HCS, are required for the prediction of advisability of home care.

scholarly journals Diagnostic Value of Combination of MicroRNA-192 in Urinary Sediment and B-Ultrasound for Bladder Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303381989457
Author(s):  
Fuquan Jiang ◽  
Changfeng Li ◽  
Jiansong Han ◽  
Linlin Wang
Keyword(s):  
Bladder Cancer ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Diagnostic Value ◽  
Urinary Sediment ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Objective: We aimed to explore the diagnostic value of microRNA-192 expression in urinary sediment combined with B-ultrasound in the diagnosis of bladder cancer. Methods: A total of 118 patients with bladder cancer and 120 patients with benign urinary system diseases were selected for collection of urinary sediment. Real-time quantitative polymerase chain reaction was applied to detect the microRNA-192 expression (normalized to U6 level) in urinary sediment. Besides, the relationship between microRNA-192 expression and clinicopathological characteristics was analyzed. Furthermore, receiver operating characteristic curve was performed to analyze clinical value of microRNA-192 expression alone and microRNA-192 expression in urinary sediment combined with B-ultrasound in the diagnosis of bladder cancer. Results: MicroRNA-192 expression was significantly downregulated in urinary sediment of patients with bladder cancer, which was related to tumor stage and tumor size ( P < .05). The results of receiver operating characteristic curve analysis showed that the best critical value of microRNA-192 expression in urinary sediment for the diagnosis of bladder cancer was 0.785 with the sensitivity and specificity of 76.7% and 78.0%, respectively. The sensitivity and specificity of microRNA-192 expression in urinary sediment combined with B-ultrasound in the diagnosis of bladder cancer were 93.2% and 76.7%, respectively. The sensitivity of combined diagnosis (93.2%) was not significantly different from that of cystoscopy (93.2%; P > 0.05). There were significant differences between the expression of microRNA-192 in urinary sediment and the sensitivity of B-ultrasound examination alone with cystoscopy ( P < .05). Conclusion: The downregulation of microRNA-192 expression in urinary sediment of patients with bladder cancer may be related to tumor progression. The microRNA-192 expression in urinary sediment is valuable in the diagnosis of bladder cancer, which shows high sensitivity in diagnosis of bladder cancer when combined with B-ultrasound.

Sign in / Sign up

Export Citation Format

Share Document