On Thu, 11 Mar 2004 13:16:15 -0500 XIAO LIU ,xiaoliu at jhmi.edu, wrote: , Dear R-helpers: , , I want to calculate area under a Receiver Operator Characteristic curve. , Where can I find related functions? , , Thank you in advance , , Xiao , install.packages(Hmisc) library(Hmisc) w ,- somers2(predicted probability, 0/1 diagnosis) Convert Somers Dxy rank correlation to ROC area (C) using Dxy=2*(C-.5). To get standard error of Dxy (and hence C) type ?rcorr.cens (another Hmisc function). This is the nonparametric Wilcoxon-Mann-Whitney approach. --- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ...
Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+
ROC curve is used to evaluate classification models. Learn threshold tuning, ROC curve in Machine Learning,area under roc curve , and ROC curve analysis in Python.
TY - JOUR. T1 - Mutual information as a performance measure for binary predictors characterized by both roc curve and proc curve analysis. AU - Hughes, Gareth. AU - Kopetzky, Jennifer. AU - McRoberts, Neil. PY - 2020/9. Y1 - 2020/9. N2 - The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and ...
How is the cutoff point actually determined? This can be based on a pure mathematical analysis to either minimize the mathematical distance between the receiver operator characteristic curve and the ideal point (sensitivity = specificity = 1) or to maximize the difference (Youden index) between the receiver operator characteristic curve and the diagonal or chance line (sensitivity + specificity − 1). Whereas the first approach minimizes misclassification, the second one maximizes appropriate classification.4 An alternative approach will also take into account the cost-benefit ratio of the consequence of having either false-positive or false-negative results. One can easily appreciate that in certain pathologic conditions, treatment of a patient with a false-positive result may be worse than missing a false-negative result or conversely that not treating a patient with a false-negative result may be worse than treating one with a false-positive result. In the case of PPV assessment, a low ...
Clinical practice commonly demands yes or no decisions; and for this reason a clinician frequently needs to convert a continuous diagnostic test into a dichotomous test. Receiver operating characteristic (ROC) curve analysis is an important test for assessing the diagnostic accuracy (or discrimina …
Decided to start githib with ROC curve plotting example. There is not a one ROC curve but several - according to the number of comparisons (classifications), also legend with maximal and minimal ROC AUC are added to the plot. ROC curves and ROC AU...
In receiver operating characteristic ROC curve analysis, the optimal cutoff value for a diagnostic test can be found on the ROC curve where the slope of the curve is equal to C/B x 1-pD/pD, where pD is the disease prevalence and C/B is the ratio of net costs of treating nondiseased individuals to net benefits of treating diseased individuals....
from sklearn.metrics import roc_curve, auc ### Fit a sklearn classifier on train dataset and output probabilities pred_val = svc.predict_proba(self.X_test)[:,1] ### Compute ROC curve and ROC area for predictions on validation set fpr, tpr, _ = roc_curve(self.y_test, pred_val) roc_auc = auc(fpr, tpr) ### Plot plt.figure() lw = 2 plt.plot(fpr, tpr, color=darkorange, lw=lw, label=ROC curve (area = %0.2f) % roc_auc) plt.plot([0, 1], [0, 1], color=navy, lw=lw, linestyle=--) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel(False Positive Rate) plt.ylabel(True Positive Rate) plt.title(Receiver operating characteristic example) plt.legend(loc=lower right) plt.show ...
A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the rating method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly …
Read The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Receiver operating characteristic (ROC) curve is an effective and widely used method for evaluating the discriminating power of a diagnostic test or statistical model. As a useful statistical method, a wealth of literature about its theories and computation methods has been established. The research on ROC curves, however, has focused mainly on cross-sectional design. Very little research on estimating ROC curves and their summary statistics, especially significance testing, has been conducted for repeated measures design. Due to the complexity of estimating the standard error of a ROC curve, there is no currently established statistical method for testing the significance of ROC curves under a repeated measures design. In this paper, we estimate the area of a ROC curve under a repeated measures design through generalized linear mixed model (GLMM) using the predicted probability of a disease or positivity of a condition and propose a bootstrap method to estimate the standard error of the area ...
OBJECTIVE:To assess the relationships between fluid and imaging biomarkers of tau pathology and compare their diagnostic utility in a clinically heterogeneous sample. METHODS:Fifty-three patients (28 with clinical Alzheimer disease [AD] and 25 with non-AD clinical neurodegenerative diagnoses) underwent β-amyloid (Aβ) and tau ([18F]AV1451) PET and lumbar puncture. CSF biomarkers (Aβ42, total tau [t-tau], and phosphorylated tau [p-tau]) were measured by multianalyte immunoassay (AlzBio3). Receiver operator characteristic analyses were performed to compare discrimination of Aβ-positive AD from non-AD conditions across biomarkers. Correlations between CSF biomarkers and PET standardized uptake value ratios (SUVR) were assessed using skipped Pearson correlation coefficients. Voxelwise analyses were run to assess regional CSF-PET associations. RESULTS:[18F]AV1451-PET cortical SUVR and p-tau showed excellent discrimination between Aβ-positive AD and non-AD conditions (area under the curve ...
Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods. We assessed the performance of our method using data from a publicly available database of 4026 genes, including 14 normal B cell samples (NBC) and 20 heterogeneous lymphomas (namely: 9
Compared to those with bacterial or mixed infection (n = 9), patients with 2009 H1N1 infection (n = 16) were significantly more likely to have bilateral chest X-ray infiltrates, lower APACHE scores, more prolonged lengths of stay in ICU and lower white cell count, procalcitonin and CRP levels. Using a cutoff of ,0.8 ng/ml, the sensitivity and specificity of procalcitonin for detection of patients with bacterial/mixed infection were 100 and 62%, respectively. A CRP cutoff of ,200 mg/l best identified patients with bacterial/mixed infection (sensitivity 100%, specificity 87.5%). In combination, procalcitonin levels ,0.8 ng/ml and CRP ,200 mg/l had optimal sensitivity (100%), specificity (94%), negative predictive value (100%) and positive predictive value (90%). Receiver-operating characteristic curve analysis suggested the diagnostic accuracy of procalcitonin may be inferior to CRP in this setting.. ...
On Jun 26, 2017, at 11:40 AM, Brian Smith ,bsmith030465 at gmail.com, wrote: , , Hi, , , I was trying to draw some ROC curves (prediction of case/control status), , but seem to be getting a somewhat jagged plot. Can I do something that , would smooth it somewhat? Most roc curves seem to have many incremental , changes (in x and y directions), but my plot only has 4 or 5 steps even , though there are 22 data points. Should I be doing something differently? , , How can I provide a URL/attachment for my plot? Not sure if I can provide , reproducible code, but here is some pseudocode, let me know if youd like , more details: , , ##### , ## generate roc and auc values , ##### , library(pROC) , library(AUCRF) , , getROC ,- function(d1train,d1test){ , my_model ,- AUCRF(formula= status ~ ., data=d1train, , ranking=MDA,ntree=1000,pdel=0.05) , my_opt_model ,- my_model$RFopt , , my_probs ,- predict(my_opt_model, d1test, type = prob) , my_roc ,- roc(d1test[,resp_col] ~ my_probs[,2]) , aucval ,- ...
The introduction of digital CXR with automated computer-aided interpretation, has given impetus to the role of CXR in TB screening, particularly in low resource, high-burden settings. The aim of this study was to evaluate the diagnostic accuracy of CAD4TB as a screening tool, implemented in the private sector in Karachi, Pakistan. This study analyzed retrospective data from CAD4TB and Xpert MTB/RIF testing carried out at two private TB treatment and diagnostic centers in Karachi. Sensitivity, specificity, potential Xperts saved, were computed and the receiver operator characteristic curves were constructed for four different models of CAD4TB. A total of 6,845 individuals with presumptive TB were enrolled in the study, 15.2% of which had MTB + ve result on Xpert. A high sensitivity (range 65.8-97.3%) and NPV (range 93.1-98.4%) were recorded for CAD4TB. The Area under the ROC curve (AUC) for CAD4TB was 0.79. CAD4TB with patient demographics (age and gender) gave an AUC of 0.83. CAD4TB offered high ...
Life is full of surprises. When I was looking at whether the software package R could compute and analyze Receiver Operating Characteristic (ROC) curves, I found out that there is an application of ROC curves for microarray data. Apparently, the positive false discovery rate can be conceived of in a diagnostic testing format as relating to the positive predictive value. I have not had time to read all the details, but there is a nice paper on this at. ...
To determine if the manufacturers cutoff criteria were optimal, ROC curves were generated. The ViraBlot assay produced an ROC curve with an area under the curve (AUC) of 0.988 (P , 0.0001). The optimal cutoff criterion for maximum sensitivity and specificity matched the manufacturers protocol. For the Virotech assay, an ROC curve with an AUC of 0.987 (P , 0.0001) was produced. This ROC curve indicated that by reducing the cutoff criterion by one band, the sensitivity could be increased from 90.0% to 98.4% (95% CI, 93.6 to 99.7%) without significantly decreasing the specificity. This would reduce the number of false-negative results. The Marblot assay produced an ROC curve with an AUC of 0.988 (P , 0.0001). The ROC curve indicated that by reducing the cutoff criterion by one, the number of equivocal results would decrease from 25 to 14 without significantly decreasing sensitivity or specificity. However, this is still an unacceptably high number of equivocal samples.. Although FTA-ABS testing ...
Paul R. Yarnold Optimal Data Analysis, LLC Receiver operator characteristic (ROC) analysis is sometimes used to assess the classification accuracy achieved using an ordered attribute to discriminate a dichotomous class variable, and in this context to identify an
TY - GEN. T1 - Optimizing sensitivity-resolution trade-off using generalized detection/discrimination task and three-class ROC analysis. AU - Volokh, Lana. AU - He, Xin. AU - Frey, Eric C.. AU - Tsui, B. M.W.. PY - 2006/1/1. Y1 - 2006/1/1. N2 - The goal of this work is to address the problem of quantifying sensitivity/resolution trade-off in emission imaging system optimization. We propose a task and a figure of merit (FOM) based on 3-class ROC analysis. The task combines detection of a lesion on a variable background with discrimination of a lesion from an adjacent structure, modeled by Rayleigh task. Recently developed practical framework for conducting 3-class ROC analysis is utilized to evaluate the performance of channelized hotelling observer. A primitive imaging system model is used to investigate effects of resolution, sensitivity and imaging time. Volume under ROC surface (VUS) is used as the primary FOM in assessment of the task performance. The FOM is optimal according to several ...
After 2.4 ± 2.1 years, there were 11 cardiac deaths (event rate 7.6%/year). The causes of death were worsening congestive heart failure and arrhythmia. Fatal or nonfatal myocardial infarctions were not observed. Twelve patients died of noncardiac causes (8 due to infections) and were censored at the time of death. The overall survival rate at the end of the study period was 62%. Receiver-operating characteristic curve analysis demonstrated a significant association between ΔWMSI and cardiac death. A cut point value for ΔWMSI of 0.38 predicted cardiac death with a specificity of 88% and a sensitivity of 73% (area under the curve = 0.75, 95% CI: 0.54 to 0.97; p = 0.01). Using this cut point value of ΔWMSI, we stratified the study group into patients with ICR and patients without ICR. There were no significant differences between the groups in the presence of cardiovascular risk factors or the use of antiremodeling medications. The group without ICR was more frequently taking diuretics (75% vs. ...
The majority of patients develop resistance against suppression of HER2-signaling mediated by trastuzumab in HER2 positive breast cancer (BC). HER2 overexpression activates multiple signaling pathways, including the mitogen-activated protein kinase (MAPK) cascade. MAPK phosphatases (MKPs) are essential regulators of MAPKs and participate in many facets of cellular regulation, including proliferation and apoptosis. We aimed to identify whether differential MKPs are associated with resistance to targeted therapy in patients previously treated with trastuzumab. Using gene chip data of 88 HER2-positive, trastuzumab treated BC patients, candidate MKPs were identified by Receiver Operator Characteristics analysis performed in R. Genes were ranked using their achieved area under the curve (AUC) values and were further restricted to markers significantly associated with worse survival. Functional significance of the two strongest predictive markers was evaluated in vitro by gene silencing in HER2 ...
We performed the iterative outlier method on 373 cognitively unimpaired (CU) subjects and calculated the optimal cutoff value for Aβ positivity. The validation was performed using the independent dataset, comprising 83 subjects (27 CU, 27 amnestic mild cognitive impairment, and 29 Alzheimers dementia). We evaluated the validity of the Aβ cutoff value by calculating its concordance rate with the visual assessment and between two different Aβ tracers performed in the same subject ...
Ch17-18 (ROC Curve: receiver operating characteristics, curving up and to…: Ch17-18 (ROC Curve: receiver operating characteristics, curving up and to the left is better unless its a corner (target leakage) , Still minimal understanding of models beyond introductory info, CH18: comparing model pairs, AOC= area under curve, good= low FPR and high TPR)
Receiver Operating Characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models to support decision, diagnosis, and prognosis. ROC analysis investigates the accuracy of models and ability to separate positive from negative cases. It is especially useful in evaluating predictive models and in comparing ...
TY - GEN. T1 - Rate-oriented point-wise confidence bounds for ROC curves. AU - Millard, Louise A C. AU - Kull, Meelis. AU - Flach, Peter A.. PY - 2014/1/1. Y1 - 2014/1/1. N2 - Common approaches to generating confidence bounds around ROC curves have several shortcomings. We resolve these weaknesses with a new rate-oriented approach. We generate confidence bounds composed of a series of confidence intervals for a consensus curve, each at a particular predicted positive rate (PPR), with the aim that each confidence interval contains new samples of this consensus curve with probability 95%. We propose two approaches; a parametric and a bootstrapping approach, which we base on a derivation from first principles. Our method is particularly appropriate with models used for a common type of task that we call rate-constrained, where a certain proportion of examples needs to be classified as positive by the model, such that the operating point will be set at a particular PPR value. © 2014 ...
Someone asked me about how to use an ROC curve if you have more than two categories. Apparently the gold standard that the researchers were using was known to be imperfect, so they wanted an intermediate category (possible disease).. There s a lot of literature about less than perfect gold standards, and you should familiarize yourself with that first. Creating an intermediate category is not the best way to handle an imperfect gold standard. Often the best approach when there is an imperfect gold standard is to apply a second or third different (but still imperfect, of course) gold standard.. As far as I know, there is no way to adapt the ROC curve to more than two groups. You can, however, use a different model, such as ordinal logistic regression to see how well your diagnostic test predicts in the three categories.. If all of this seems too complicated, consider dropping the middle group or combining it with one of the other two groups. You already know that it is less than ideal, but it may ...
ROC curves of the SPAN, TSQ and IES-R for 6 month PTSD.Note: ROC curves represent original sensitivity and specificity values using linear interpolation between
Hi guys! I am trying to determine the best cut off value for a single diagnostic test by performing the ROC curve analysis. The diagnostic test has AUC of...
Use ROC curves to assess classification models. Walk through several examples that illustrate what ROC curves are and why youd use them.
The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendalls tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.. ...
ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. A: ROC curves for phenotypic similari
The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the
Function to estimate the ROC Curve of a continuous-scaled diagnostic test with the help of a second imperfect diagnostic test with binary responses.
Excel tool for Analysis of single ROC curve (receiver operating characteristics): Graph, calculation of AUC incl. confidence intervals
ROC/AUC methods. fast.auc calculates the AUC using a sort operation, instead of summing over pairwise differences in R. computeRoc computes an ROC curve. plotRoc plots an ROC curve. addRoc adds an ROC curve to a plot. classification.error computes classification error
A ROC curve is a graph that plots true positive rates against false positive rates for a series of cutoff values, or in other words, graphically displays the trade-off between sensitivity and specificity for each cutoff value. An ideal cutoff might give the test the highest possible sensitivity with the lowest possible false positive rate (i.e., highest specificity). This is the point lying geometrically closest to the top-left corner of the graph (where the ideal cutoff value with 100% sensitivity and specificity would be plotted). Picking the ideal cutoff score is, to some extent, dependent on the clinical context, that is the purpose for which the tool will be used. The area under an ROC curve can be used as an overall estimate of its discriminating ability and sometimes is expressed as accuracy. The area under the ROC curve is equal to the probability that a test correctly classifies patients as true positives or true negatives. Greater areas under the curve indicate higher accuracy. To ...
To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) ,80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. ...
Studies evaluating a new diagnostic imaging test may select control subjects without disease who are similar to case subjects with disease in regard to factors potentially related to the imaging result. Selecting one or more controls that are matched to each case on factors such as age, comorbidities, or study site improves study validity by eliminating potential biases due to differential characteristics of readings for cases versus controls. However, it is not widely appreciated that valid analysis requires that the receiver operating characteristic (ROC) curve be adjusted for covariates. We propose a new computationally simple method for estimating the covariate-adjusted ROC curve that is appropriate in matched case-control studies.. We provide theoretical arguments for the validity of the estimator and demonstrate its application to data. We compare the statistical properties of the estimator with those of a previously proposed estimator of the covariate-adjusted ROC curve. We demonstrate an ...
Figure 3: Receiver operator characteristic curve (ROC) assessing the validity of the anti-MCV test in diagnosing RA. The area under the curve was 0.893 at the 95% CI. The sensitivity of each test is plotted against one minus specificity for varying cutoffs (values lower than the cutoff were considered negative, and other values were considered positive) (n= 40 ...
In my ROC curve analysis output, on the table «Coordinates of the curve» there is a footnote saying « All the other cutoff values are the averages of two consecutive ordered observed test values». How can I know the Sensitivity and Specificity for each OBSERVED VALUE (not for means of observed values ...
This example shows how you can assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves.
This example shows how you can assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves.
This example shows how you can assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves.
BACKGROUND Fatal cases of COVID-19 are increasing globally. We retrospectively investigated the potential of immunologic parameters as early predictors of COVID-19.METHODS A total of 1018 patients with confirmed COVID-19 were enrolled in our 2-center retrospective study. Clinical feature, laboratory test, immunological test, radiological findings, and outcomes data were collected. Univariate and multivariable logistic regression analyses were performed to evaluate factors associated with in-hospital mortality. Receiver operator characteristic (ROC) curves and survival curves were plotted to evaluate their clinical utility.RESULTS The counts of all T lymphocyte subsets were markedly lower in nonsurvivors than in survivors, especially CD8+ T cells. Among all tested cytokines, IL-6 was elevated most significantly, with an upward trend of more than 10-fold. Using multivariate logistic regression analysis, IL-6 levels of more than 20 pg/mL and CD8+ T cell counts of less than 165 cells/μL were found ...
Dirk Tasche of Deutsche Bundesbank. November 2002. Abstract: Assessing the discriminative power of rating systems is an important question to banks and to regulators. In this article we analyze the Cumulative Accuracy Profile (CAP) and the Receiver Operating Characteristic (ROC) which are both commonly used in practice. We give a test-theoretic interpretation for the concavity of the CAP and the ROC curve and demonstrate how this observation can be used for more efficiently exploiting the informational contents of accounting ratios. Furthermore, we show that two popular summary statistics of these concepts, namely the Accuracy Ratio and the area under the ROC curve, contain the same information and we analyse the statistical properties of these measures. We show in detail how to identify accounting ratios with high discriminative power, how to calculate confidence intervals for the area below the ROC curve, and how to test if two rating models validated on the same data set are different. All ...
Background: Due to the faltering sensitivity and/or specificity, urine-based assays currently have a limited role in the management of patients with bladder cancer (BCa). The aim of this study was to externally validate our previously reported protein biomarker panel from multiple sites in the US and Europe. Methods: This multicenter external validation study included a total of 320 subjects (BCa = 183). The 10 biomarkers (IL8, MMP9, MMP10, SERPINA1, VEGFA, ANG, CA9, APOE, SDC1 and SERPINE1) were measured using commercial ELISA assays in an external laboratory. The diagnostic performance of the biomarker panel was assessed using receiver operator curves (ROC) and descriptive statistical values. Results: Utilizing the combination of all 10 biomarkers, the AUROC for the diagnostic panel was noted to be 0.847 [95% CI: 0.796 - 0.899], outperforming any single biomarker. The multiplex assay at optimal cutoff value achieved an overall sensitivity of 0.79, specificity of 0.79, PPV of 0.73 and NPV of ...
Patients with blunt trauma to the liver have elevated levels of liver enzymes within a short time post injury, potentially useful in screening patients for computed tomography (CT). This study was performed to define the optimal cut-off values for serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in patients with blunt liver injury diagnosed with contrast enhanced multi detector-row CT (CE-MDCT). All patients admitted from May 2006 to July 2013 to Teikyo University Hospital Trauma and Critical Care Center, and who underwent abdominal CE-MDCT within 3 h after blunt trauma, were retrospectively enrolled. Using receiver operating characteristic (ROC) curve analysis, the optimal cut-off values for AST and ALT were defined, and sensitivity and specificity were calculated. Of a total of 676 blunt trauma patients 64 patients were diagnosed with liver injury (Group LI+) and 612 patients without liver injury (Group LI−). Group LI+ and LI− were comparable for age, Revised Trauma Score,
I was part of a team conducting the ROC Curve Analysis using the state of Delawares education data. We put a lot of details in this paper, so people can replicate what we did. Appendix section has a lot of explanations regarding statistical models and concepts.. http://www.doe.k12.de.us/cms/lib09/DE01922744/Centricity/Domain/91/MA1275TAFINAL508.pdf. ...
A growing number of studies have reported a link between vascular damage and glaucoma based on optical coherence tomography angiography (OCTA) imaging. This multitude of studies focused on different regions of interest (ROIs) which offers the possibility to draw conclusions on the most discriminative locations to diagnose glaucoma. The objective of this work was to review and analyse the discriminative capacity of vascular density, retrieved from different ROIs, on differentiating healthy subjects from glaucoma patients. PubMed was used to perform a systematic review on the analysis of glaucomatous vascular damage using OCTA. All studies up to 21 April 2019 were considered. The ROIs were analysed by region (macula, optic disc and peripapillary region), layer (superficial and deep capillary plexus, avascular, whole retina, choriocapillaris and choroid) and sector (according to the Garway-Heath map). The area under receiver operator characteristic curve (AUROC) and the statistical difference ...
Objective: To assess the association of BMI and waist circumference (WC) with metabolic risk factors, and confirm the appropriate cut-off points of BMI and WC among Chinese adults. Methods: After excluding participants with missing or extreme measurement values, as well as individuals with self-reported histories of cancer, a total of 501 201 adults in baseline and 19 201 adults in the second re-survey from the China Kadoorie Biobank were included. The associations of BMI and WC with metabolic risk factors were estimated. Receiver operating characteristic (ROC) analyses were conducted to assess the appropriate cut-off values of BMI and WC to predict the risk of hypertension, diabetes, dyslipidemia and clustering of risk factors. Results: The prevalence of hypertension, diabetes, dyslipidemia and clustering of risk factors all presented ascending trends with the increasing levels of BMI or WC. Defined as the points on the ROC curve where Youdens index reached the highest, the appropriate overweight cut
Results 920 patients were recruited: mean (SD) age was 73.1 (10.0) years; 53.9% were female subjects; mean (SD) forced expiratory volume in one second was 43.6 (17.2) % predicted; and 96 patients (10.4%) died in hospital. The five strongest predictors of mortality (extended MRC Dyspnoea Score, eosinopenia, consolidation, acidaemia, and atrial fibrillation) were combined to form the Dyspnoea, Eosinopenia, Consolidation, Acidaemia and atrial Fibrillation (DECAF) Score. The Score, which underwent internal bootstrap validation, showed excellent discrimination for mortality (area under the receiver operator characteristic curve =0.86, 95% CI 0.82 to 0.89) and performed more strongly than other clinical prediction tools. In the subgroup of patients with coexistent pneumonia (n=299), DECAF was a significantly stronger predictor of mortality than CURB-65.. ...
Results. Sixteen patients (32%) developed ESLD during 173.5 ± 64.7 months of followup. Elevated serum Krebs von den Lungen-6 (KL-6) at initial assessment was highly correlated with ESLD development (p = 0.0002). Receiver-operating characteristic curve analysis revealed that a KL-6 value of 1273 U/ml effectively discriminated patients who developed ESLD from those who did not. Patients with KL-6 , 1273 U/ml were less likely to remain ESLD-free compared with those with lower KL-6 levels (p , 0.0001). Multivariate analysis showed that KL-6 , 1273 U/ml was the most reliable predictor of ESLD development (OR 51.2, 95% CI 7.6-343, p , 0.0001). Finally, the initial KL-6 level correlated with the forced vital capacity (FVC) decline rate (r = 0.58, p , 0.0001). ...
Existing neonatal treatment intensity models can predict mortality and morbidity. Discriminatory performance as measured by the area under the receiver operating curve is poorest for long-term morbidity (0.59) and highest for in-hospital mortality in infants weighing 1000-1499 g for NTISS measured at 72 h post admission (0.958) [34, 39]. Calibration was reported by Gray et al. only who found a close agreement between observed and predicted in-hospital mortality by the Hosmer-Lemeshow test [19]. Using a variety of tests of statistical association rather than predictive performance, treatment intensity was found also to be associated with mortality, morbidity and resource utilisation [19, 35, 37, 39, 40].. Discriminatory performance of neonatal treatment intensity models for predicting in-hospital mortality measured by AUROC ranges from 0.749 to 0.958 [38, 39]. The recommended threshold for good discrimination is 0.8 [11]. The reported performance therefore suggests that the treatment scoring ...
Aims: To determine whether circulating multiple miRNAs can be used as novel biomarkers for the diagnosis in breast cancer, we performed a systematic review and meta-analysis. Materials & methods: After searching the databases of PubMed, EMBASE and Web of Science, we used the bivariate meta-analysis model to summarize the diagnostic indices and plot the summary receiver operator characteristic curve. Results: ...
The study, which looked at intermediate-risk participants (FRS _5%-_20%) in the Multi-Ethnic Study of Atherosclerosis (MESA), found that overall CAC, ABI, high-sensitivity CRP, and family history were independently associated with incident CHD in multivariable analyses (HR, 2.60 [95% CI, 1.94-3.50]; HR, 0.79 [95% CI, 0.66-0.95]; HR, 1.28 [95% CI, 1.00-1.64]; and HR, 2.18 [95% CI, 1.38-3.42], respectively). CAC had the highest improvement in both the area under the receiver operator characteristic curves and net reclassification improvement when added to the Framingham Risk Score/Reynolds score, while brachial flow-mediated dilation had the least. Carotid intima-media thickness and brachial flow-mediated dilation were not associated with incident CHD in multivariable analyses, according to the authors. ...
Results: We identified 18 validation studies (n = 7180) conducted in various clinical settings. Eleven studies provided details about the diagnostic properties of the questionnaire at more than one cut-off score (including 10), four studies reported a cut-off score of 10, and three studies reported cut-off scores other than 10. The pooled specificity results ranged from 0.73 (95% confidence interval [CI] 0.63-0.82) for a cut-off score of 7 to 0.96 (95% CI 0.94-0.97) for a cut-off score of 15. There was major variability in sensitivity for cut-off scores between 7 and 15. There were no substantial differences in the pooled sensitivity and specificity for a range of cut-off scores (8-11). ...
The pooled data reported in Table 1 summarize the ability of different imaging modalities to predict the recovery of global LV function (sensitivity and specificity values) after revascularization in a population, with a prevalence of viability assessed by the optimal cutoff (ie, number of viable segments) identified by the receiver-operating curve analysis.22,49,60,62-66,70,72 The generally accepted opinion18,82 that SPECT and PET demonstrate higher sensitivity is confirmed. On the other hand, in this population, DSE has superior specificity and lower positive predictive value.64 Negative predictive value is similar for both DSE and SPECT. The importance of the definition of the cutoff value has to be highlighted. In fact, shifting the optimal cutoff value to a higher number of viable segments can improve specificity but at the expense of a decline in sensitivity.62 A higher prevalence of viability also will result in a higher positive predictive value and a lower negative predictive value. The ...
Main Outcome Measure(s) : We evaluated the specificity and sensitivity of the average strength (STR) and global efficiency (GE) of EEG α-band functional networks for classifying consciousness. A significant neural response was defined as being within one standard deviation of the healthy control mean. As a secondary outcome measure, we also compared areas under receiver operator characteristic curves (AUC). These curves were generated after calculating the sensitivities and specificities at several significance cutoffs.. ...
Mild cognitive impairment (MCI) is usually characterized by memory space loss in the absence of dementia and is considered the translational stage between normal aging and early Alzheimers disease (AD). predictive ideals, as well as receiver operator characteristic (ROC) curves, likelihood ratios and accuracy were identified for these proteins. Although the levels of ASC were higher in MCI and AD than in age-matched settings, protein levels of ASC were higher in MCI than in AD instances. For control vs. MCI, the area under the curve (AUC) for ASC was 0.974, having a cut-off point of 264.9 pg/mL. These data were comparable to the AUC for sAPP and of 0.9687 and 0.9068, respectively, as well as 0.7734 for NfL. Moreover, similar results were acquired for control vs. AD and MCI vs. AD. These results indicate that ASC is definitely a encouraging biomarker of MCI and AD. = 66 control, 32 MCI and 31 AD. IL-18: = 69 control, 31 MCI and 32 AD. Package and whiskers are demonstrated for the 5th and 95th ...
The diagnostic odds ratio ranges from zero to infinity. A diagnostic odds ratio of exactly one means that the test is equally likely to be positive whether someone has the condition or not ...
A ROC curve is a diagnostic plot that evaluates a set of probability predictions made by a model on a test dataset.. A set of different thresholds are used to interpret the true positive rate and the false positive rate of the predictions on the positive (minority) class, and the scores are plotted in a line of increasing thresholds to create a curve.. The false-positive rate is plotted on the x-axis and the true positive rate is plotted on the y-axis and the plot is referred to as the Receiver Operating Characteristic curve, or ROC curve. A diagonal line on the plot from the bottom-left to top-right indicates the curve for a no-skill classifier (predicts the majority class in all cases), and a point in the top left of the plot indicates a model with perfect skill.. The curve is useful to understand the trade-off in the true-positive rate and false-positive rate for different thresholds. The area under the ROC Curve, so-called ROC AUC, provides a single number to summarize the performance of a ...
Digit ratio (2D:4D) denotes the relative length of the second and fourth digits. There are contradicting reports on its relationship with ethnicity/race, whereas convincing studies show it is related to obesity. This cross-sectional study was undertaken to demystify ethnic difference in 2D:4D ratio and to analyze its relationship with obesity among adults in Ilorin Nigeria. The cross-sectional study included 701 individuals. Finger lengths were measured with electronic calipers and other anthropometric traits were measured with standard procedure. Student t test and one-way ANOVA were used to detect differences among groups and relationship was computed with Pearson correlation. The receiver operator characteristic curves were used to detect the diagnostic effect of 2D:4D for obesity. The obtained results showed sexual dimorphism in 2D:4D ratio and other anthropometrics at p , 0.01. Obesity was associated with significantly higher mean of 2D:4D in both genders (female 0.9814 ± 0.012:0.9700 ± ...
Recent successful discoveries of potentially causal single nucleotide polymorphisms (SNPs) for complex diseases hold great promise, and commercialization of genomics in personalized medicine has already begun. The hope is that genetic testing will benefit patients and their families, and encourage positive lifestyle changes and guide clinical decisions. However, for many complex diseases, it is arguable whether the era of genomics in personalized medicine is here yet. We focus on the clinical validity of genetic testing with an emphasis on two popular statistical methods for evaluating markers. The two methods, logistic regression and receiver operating characteristic (ROC) curve analysis, are applied to our age-related macular degeneration dataset. By using an additive model of the CFH, LOC387715, and C2 variants, the odds ratios are 2.9, 3.4, and 0.4, with p-values of 10−13, 10−13, and 10−3, respectively. The area under the ROC curve (AUC) is 0.79, but assuming prevalences of 15%, 5.5%, and 1.5%
TY - JOUR. T1 - Additive value of biomarkers and echocardiography to stratify the risk of death in heart failure patients with reduced ejection fraction. AU - Falletta, Calogero. AU - Clemenza, Francesco. AU - Klersy, Catherine. AU - Agnese, Valentina. AU - Bellavia, Diego. AU - Di Gesaro, Gabriele. AU - Minà, Chiara. AU - Romano, Giuseppe. AU - Temporelli, Pier Luigi. AU - Dini, Frank Lloyd. AU - Rossi, Andrea. AU - Raineri, Claudia. AU - Turco, Annalisa. AU - Traversi, Egidio. AU - Ghio, Stefano. AU - Salzano, Andrea. PY - 2019/1/1. Y1 - 2019/1/1. N2 - Background. Risk stratification is a crucial issue in heart failure. Clinicians seek useful tools to tailor therapies according to patient risk. Methods. A prospective, observational, multicenter study on stable chronic heart failure outpatients with reduced left ventricular ejection fraction (HFrEF). Baseline demographics, blood, natriuretic peptides (NPs), high-sensitivity troponin I (hsTnI), and echocardiographic data, including the ratio ...
This Demonstration compares the ratios of the areas under the curve (AUC) and the ratios of the areas over the curve (AOC) of the receiver operating characteristic (ROC) plots of two diagnostic tests (ratio of the AUC of the first test to the AUC of the second test: blue plot, ratio of the AOC of the first test to the AOC of the second test: orange plot). The two tests measure the same measurand,
A cerebrospinal fluid (CSF)-mask algorithm has been developed to reduce the adverse influence of CSF-low-counts on the diagnostic utility of the specific binding ratio (SBR) index calculated with Southampton method. We assessed the effect of the CSF-mask algorithm on the diagnostic performance of the SBR index for parkinsonian syndromes (PS), including Parkinsons disease, and the influence of cerebral ventricle dilatation on the CSF-mask algorithm. We enrolled 163 and 158 patients with and without PS, respectively. Both the conventional SBR (non-CSF-mask) and SBR corrected with the CSF-mask algorithm (CSF-mask) were calculated from 123I-Ioflupane single-photon emission computed tomography (SPECT) images of these patients. We compared the diagnostic performance of the corresponding indices and evaluated whether the effect of the CSF-mask algorithm varied according to the extent of ventricle dilatation, as assessed with the Evans index (EI). A receiver-operating characteristics (ROC) analysis was used
Purpose: To develop a quantitative decision making metric for automatically detecting irregular breathing using a large patient population that received phase-sorted 4DCT. Methods: This study employed two patient cohorts. Cohort#1 contained 256 patients who received a phasesorted 4DCT. Cohort#2 contained 86 patients who received three weekly phase-sorted 4DCT scans. A previously published technique used a single abdominal surrogate to calculate the ratio of extreme inhalation tidal volume to normal inhalation tidal volume, referred to as the κ metric. Since a single surrogate is standard for phase-sorted 4DCT in radiation oncology clinical practice, tidal volume was not quantified. Without tidal volume, the absolute κ metric could not be determined, so a relative κ (κrel) metric was defined based on the measured surrogate amplitude instead of tidal volume. Receiver operator characteristic (ROC) curves were used to quantitatively determine the optimal cutoff value (jk) and efficiency cutoff ...
Measuring the accuracy of diagnostic tests is crucial in many application areas, in particular medicine and health care. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. Nonparametric predictive inference (NPI) is a frequentist statistical method that is explicitly aimed at using few modelling assumptions in addition to data, enabled through the use of lower and upper probabilities to quantify uncertainty. It focuses exclusively on a future observation, which may be particularly relevant if one considers decisions about a diagnostic test to be applied to a future patient. The NPI approach to three-group ROC analysis is presented, including results on the volumes under the ROC surfaces and choice of decision threshold for the diagnosis. ...
Circular RNAs (circRNAs) have recently emerged as a new class of RNAs, highly enriched in the brain and very stable within cells, exosomes and body fluids. In this study, we aimed to screen the exosome derived circRNAs in glioblastoma multiforme (GBM) and investigate whether these circRNAs could predict GBM as potential biomarkers. The exosome was extracted from the plasma of GBM patients and healthy volunteers and validated by immunoblotting. The circRNA microarray was employed with three samples in each group to screen the dysregulated circRNAs isolated from the exosome. Five circRNAs were first selected as candidates with the upregulated level in exosome isolated from the plasma of GBM. Further validation found that only hsa_circ_0055202, hsa_circ_0074920 and hsa_circ_0043722 were consistent with training set. The Receiver operating characteristic (ROC) curve also revealed a high diagnostic ability an area under ROC curve value (AUC) for single circRNA and combined. The AUC for hsa_circ_0055202, hsa
Receiver operating characteristic (ROC) analysis of nerve messages is described. The hypothesis that quantum fluctuations provide the only limit to the ability of frog ganglion cells to signal luminance change information is examined using ROC analysis. In the context of ROC analysis, the quantum fluctuation hypothesis predicts (a) the detectability of a luminance change signal should rise proportionally to the size of the change, (b) detectability should decrease as the square root of background, an implication of which is the deVries-Rose law, and (c) ROC curves should exhibit a shape particular to underlying Poisson distributions. Each of these predictions is confirmed for the responses of dimming ganglion cells to brief luminance decrements at scotopic levels, but none could have been tested using classical nerve message analysis procedures. ...
We have developed, evaluated and validated clinical diagnostic models combining age at diagnosis, BMI, GADA, IA-2 and T1D GRS to provide estimates of a patients risk of having type 1 diabetes requiring rapid insulin therapy from diagnosis. These models show high performance and could potentially assist classification of diabetes in clinical practice and provide a tool for evidence-based classification in research cohorts.. Model performance was optimised in the model combining all five predictors (ROC AUC 0.97). However, all models performed well with ROC AUC ,0.9 and low cross-validated prediction errors in development. The results of the external validation provide additional confidence in model performance. This was undertaken in a distinct dataset with different type 1 diabetes prevalence and biochemical assays.. This is the first study developing clinical diagnostic models for classification of type 1 and 2 diabetes. Key strengths of this study include our systematic approach to model ...
When a test has more than two possible outcomes, its accuracy can be reported as pairs of sensitivity and specificity corresponding to each degree of abnormality. This approach, the basis for receiver-operating characteristic analysis, maximizes the use of diagnostic information (2). When the diagnostic threshold is set at a lower degree of abnormality, the sensitivity of the test tends to increase but its specificity tends to decrease. The opposite occurs when a higher diagnostic threshold is selected. In the case of HIV viral load assays, if more viral units must be detected to report the test result as abnormal, the specificity will increase. As noted by Rich and colleagues, the lowest reported plasma viral load during seroconversion is more than 17 times higher than the highest viral load detected in our three patients. Thus, for the diagnosis of acute infection, the threshold should probably be set much higher ...
Atio; CI, Confidence Interval; AUC, area under the ROC curve. a Odds Ratio for any increase of one unit. { Eledoisin web p-value of the Wald statistic.
14-3-3ơ is an intracellular, phosphoserine binding protein and proposed to be involved in tumorigenesis. However, the expression dynamics of 14-3-3ơ and its clinicopathological/prognostic significance in human tumors are still controversial. The method of immunohistochemistry (IHC) and Western blot were utilized to examine the protein expression of 14-3-3ơ in gastric cancer and paired normal adjacent gastric mucosal tissues. Receive operating characteristic (ROC) curve analysis was employed to determine a cutoff score for 14-3-3ơ expression in a training set (n = 66). For validation, the ROC-derived cutoff score was subjected to analysis of the association of 14-3-3ơ expression with patient outcome and clinical characteristics in a testing set (n = 86) and overall patients (n = 152). The expression frequency and expression levels of 14-3-3ơ were significantly higher in gastric cancer than in normal gastric mucosal tissues. Correlation analysis demonstrated that high expression of 14-3-3ơ in
Metz, C.E., Herman, B.A. and Roe, C.A. (1998) Statistical comparison of two ROC curve estimates obtained from partially-paired datasets. Medical Decision Making, 18, 110-121.
From the evidence, sensitivity and specificity can neither rule out or rule in SA (Margaretten, Li 2004). The value at which these statistical tests have been based is not always noted and comparable among different subsets of patients studied. Although it seems that the likelihood ratio becomes more valuable diagnostically as the WBC increases, and in particular the polymorphonuclear cells, a cutoff value is what would be most use. The best evidence that involves receiver operator characteristic (ROC) analysis suggests that a value between 1500 and 2000 cells/mm3, namely polymorphonuclear cells, seems to be associated with maximum sensitivity (83%) and specificity (60 67%). This still may not be applicable to all patient groups, ie, immunocompromised (McCutchan). According to the area under the curve (AUC) ROC, WBC of the joint aspirate (jWBC) was considered fair, good and the best diagnostic test, ahead of WBC and ESR (Li, 2007). The combined sensitivity of jWBC, ESR and WBC is 100% despite ...
Looking for operating characteristic curve? Find out information about operating characteristic curve. In hypothesis testing, a plot of the probability of accepting the hypothesis against the true state of nature. Abbreviated OC curve Explanation of operating characteristic curve
Objective: In this study, tumor-stage predictive abilities of miR21, miR155, miR29a and miR92a were evaluated in rectal cancer (RC). Methods: Expression of miR21, miR155, miR29a and miR92a was detected and quantitated in tumor tissue and in adjacent normal tissue from 40 patients by TaqMan MicroRNA assay. Results: Significant overexpression of miR21, miR155, miR29a and miR92a was observed in RC tissues. While high expression of miR21, miR155 and miR29a in N1-2 and C-D stages presented a potential correlation with N and Duke stages, partial correlation analysis suggested that only miR155 rather than miR21 and miR29a played a greater influencing role. Receiver operating characteristics (ROC) curve analysis showed that miR155 could discriminate N0 from N1-2 with 85.0% sensitivity and 85.0% specificity, N2 from N0-1 with 90.0% sensitivity and 96.7% specificity, and C-D stage from A-B stage with 81.0% sensitivity and 84.2% specificity. Conclusions: Increase in expression of miR155 might represent a novel
Photographs of keratoscope rings taken at the end of BB-DALK were analyzed using ImageJ for the calculation of roundness (R): values = 1 indicate a perfect circle. Pearsons correlation was used to evaluate the relationship between R and PCA that measured 1 week (V1), 3 months (V2), and 18 months (V3), postoperatively. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the accuracy of R for identifying patients with PCA , 3 diopters (D). The point on the ROC curve nearest to the coordinate (0,100) was used as a cutoff to determine sensitivity and specificity ...
THE CHI-SQUARE GOODNESS-OF-FIT TEST The chi-square goodness-of-fit test is used to analyze probabilities of multinomial distribution trials along a single
The trade-off between sensitivity and false positive rate is often illustrated graphically as a so-called ROC curve which has false positive rate on the x-axis and sensitivity on the y-axis for varying values of the cutoff. The better a predition method is, the closer to the upper left corner the ROC curve will be, while a random (non-informative) prediction will follow the diagonal. This is an excellent way to compare different predictors, since it is not dependent on cutoff choice. Below, you can see ROC curves for SignalP 3 and 4 for the three different organism groups. Note: in contrast to the values in Table E, these are not evaluation performances; they are made by applying the finished methods to the Total data set before homology reduction ...
A likelihood ratio combines sensitivity and specificity into a single figure that indicates by how much having the test result will reduce the uncertainty of making a given diagnosis. The likelihood ratio is the probability that a given test result would occur in a person with the target disorder, divided by the probability that the same result would occur in a person without the disorder.. A positive likelihood ratio (or LR+) indicates how much more likely a person with the disease is to have a positive test result than a person without the disease.. LR+ = sensitivity / (1 - specificity),. or the ratio of true positives to false positives. Using the terms in Table 6.1, the formula is. a/(a + c) / b/(b + d).. A negative likelihood ratio (or LR-) indicates how much more likely a person without the disease is to have a negative test result, compared to a person with the disease.. LR- = (1-sensitivity) / specificity,. the ratio of false negatives to true negatives. Referring to Table 6.1, the ...
OBJECTIVE: To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD: A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to ...
Washington head coach Chris Petersen took to Pac-12 media day on Wednesday to discuss the Huskies focus on wide receivers, establishing a run game and the growing parity across the Pac-12. Check out the full transcription of the press conference here.
In [11], we first proposed an efficient algorithm, AMFES (Adaptive Multiple FEatues Selection), to select important biomarkers for cancers. Based on that initial success, this paper reports the extension of previous results on the datasets provided by Maes et al. in an attempt to discover important biomarkers for AD from the blood-based samples [12]. Unlike traditional statistical analyses, AMFES is an SVM-based methodology, which can select a much smaller subset of important biomarkers. In addition, AMFES applies an adaptive method which enables selection of a globally optimal subset of important biomarkers compared to SVM-RFE. AMFES is particularly useful for differentiating noisy biomarkers from the relevant ones when interferences between biomarkers exist. Our results are supported by a high ROC/AUC (Receiver Operating Characteristic/Area Under Curve) value when we apply a cross-validation verification. Thus, AMFES should play an important role in the classification framework of ...
Heterogeneity of the results of studies of diagnostic accuracy is common but in itself does not prevent conclusions of clinical value from being drawn.22 Despite heterogeneity being observed in the case study, it was still possible to draw a conclusion of clinical value-that an endometrial thickness of 5 mm or less can rule out endometrial cancer.. Diagnostic odds ratios and summary receiver operating characteristic curves are, however, often promoted as the most statistically valid method for combining test results when there is heterogeneity between studies, and they are commonly used in systematic reviews of diagnostic accuracy.2-4 Unfortunately summary curves are of little use to practising healthcare professionals: they can identify whether a test has potential clinical value, but they cannot be used to compute the probability of disease associated with specific test outcomes. Their use is also based on a potentially inappropriate and untested assumption that observed heterogeneity has ...
Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients prognosis to assist clinical decision-making. Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by
Results Visual analog scale disease activity (VASDA) and VAS quality of life (VASQOL) are more responsive to change in disease activity than VAS pain, morning stiffness, Health Assessment Questionnaire (HAQ), and PMR-activity score (AS). Analysis of PMR-AS versus VASDA, VASQOL, and HAQ showed correlation coefficients of 0.87 (p , 0.001), 0.80 (p , 0.001), and 0.68 (p , 0.001), respectively. Receiver-operating curve (ROC) analysis revealed VASDA to be more specific than either HAQ (0.95 vs 0.85; p , 0.001) or VASQOL (0.95 vs 0.93; p , 0.001) for the detection of response to treatment in active PMR. Overall, fibrinogen showed superior correlation coefficients with the various PRO than either of the standard biomarkers ESR or CRP. In addition, standardized response means for fibrinogen, ESR, and CRP were 1.63, 1.2, and 1.05, respectively, indicating that plasma fibrinogen was the most responsive biomarker for assessment of change in disease activity. ...
RESULTS: The maximum soft plaque component thickness proved the best discriminating factor to predict a complicated plaque by MR imaging, with a receiver operating characteristic area under the curve of 0.89. The optimal sensitivity and specificity for detection of complicated plaque by MR imaging was achieved with a soft plaque component thickness threshold of 4.4 mm (sensitivity, 0.65; specificity, 0.94; positive predictive value, 0.75; and negative predictive value, 0.9). No complicated plaque had a soft tissue plaque thickness ,2.2 mm (negative predictive value, 1) and no simple (noncomplicated) plaque had a thickness ,5.6 mm (positive predictive value, 1). ...
Results According to EuroSCORE, 51.3% (198/386), 30.1% (116/386), and 18.7% (72/386) patients were in the low-risk, intermediate-risk and high-risk group respectively; predicted mortality was 1.2% for the low-risk, 3.6% for the intermediate-risk, 6.7% for high-risk group. Actual mortality was 2.8%, 7.9% and 20.2% among the three groups respectively. Area under the ROC curve was 0.755. Hosmer-Lemeshow of fit test showed P = 0.037. According to SinoSCORE, 28.7% (111/386), 30.6% (118/386), and 40.7% (157/386) patients were in the low-risk, intermediate-risk, and high-risk group respectively; predicted mortality was 0.9% for low-risk, 2.4% for intermediate-risk, and 16.6% for high-risk group. Actual mortality was 3.3%, 4.7% and 16.5% among the three groups respectively. Area under the ROC curve of SinoSCORE was 0.783. Hosmer-Lemeshow of fit test showed P = 0.614.. ...
Sufficient replication within subpopulations is required to make the Pearson and deviance goodness-of-fit tests valid. When there are one or more continuous predictors in the model, the data are often too sparse to use these statistics. Hosmer and Lemeshow (2000) proposed a statistic that they show, through simulation, is distributed as chi-square when there is no replication in any of the subpopulations. This test is available only for binary response models. First, the observations are sorted in increasing order of their estimated event probability. The event is the response level specified in the response variable option EVENT= , or the response level that is not specified in the REF= option, or, if neither of these options was specified, then the event is the response level identified in the Response Profiles table as Ordered Value 1. The observations are then divided into approximately 10 groups according to the following scheme. Let N be the total number of subjects. Let M be the ...
Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification algorithms with the embedded validation procedures. The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves. Provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves (RCC) to estimate the classifier performance under unequal misclassification costs problem. There exists the special function to deal with missing values, including different imputing schemes.. ...
The localization receiver operating characteristic (LROC) curve is a standard method to quantify performance for the task of detecting and locating a signal. This curve is generalized to arbitrary detection/estimation tasks to give the estimation ROC (EROC) curve. For a two-alternative forced-choice study, where the observer must decide which of a pair of images has the signal and then estimate parameters pertaining to the signal, it is shown that the average value of the utility on those image pairs where the observer chooses the correct image is an estimate of the area under the EROC curve (AEROC). The ideal LROC observer is generalized to the ideal EROC observer, whose EROC curve lies above those of all other observers for the given detection/estimation task. When the utility function is nonnegative, the ideal EROC observer is shown to share many mathematical properties with the ideal observer for the pure detection task. When the utility function is concave, the ideal EROC observer makes use ...
I have recently read this:. AUC(Area Under Curve) is good for classification problems with a class imbalance. Suppose the task is to detect dementia from speech, and 99% of people dont have dementia and only 1% do. Then you can submit a classifier that always outputs no dementia, and that would achieve 99% accuracy. It would seem like your 99% accurate classifier is pretty good, when in fact it is completely useless. Using AUC scoring, your classifier would score 0.5. . Can someone please explain why does it reach 0.5? If 99% are negative and we output always no, wouldnt that mean that the TruePositiveRate will be very high and the FalsePositiveRate very low, resulting in an Area Under Curve close to one?. ...