The need for reorientation toward cost-effective prediction: Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencinaet al.,Statistics in Medicine (DOI: 10.1002/sim.2929)

2007 ◽  
Vol 27 (2) ◽  
pp. 199-206 ◽  
Author(s):  
Sander Greenland
Keyword(s):  
Roc Curve ◽  
Predictive Ability ◽  
Cost Effective

scholarly journals Yale observation scale for bacterial infection in febrile children

2020 ◽  
Vol 7 (2) ◽  
pp. 424
Author(s):  
Shiwani Mangla ◽  
Hemant Jain
Keyword(s):  
Blood Culture ◽  
Bacterial Infection ◽  
Roc Curve ◽  
Early Recognition ◽  
Predictive Ability ◽  
Cost Effective ◽  
P Value ◽  
Observation Scale ◽  
Causes Of Mortality ◽  
Yale Observation Scale

Background: Sepsis is one of the leading causes of mortality in children under 5 years by UNICEF statistics which is difficult to diagnose because of nonspecific initial clinical presentation and potential for rapid deterioration. In this regard use of Yale Observation Scale assists in early recognition of serious bacterial infection than other laboratory investigation as it is simple, quick, easy to apply and cost-effective bed side scale.Methods: All eligible young febrile infants and children were consecutively enrolled in the study. Axillary temperatures of the cases were documented. Yale observation scoring was done. Blood sample were sent for culture and sensitivity. Colonies were identified morphologically by Gram stain and biochemically. The collected data was analyzed using ROC curve for finding cut off scores of Yale Observation Scale for prediction of severe bacterial illness and final outcome. Statistical analysis was performed using the Statistical Packages for Social Sciences (SPSS) version 14 for MS Window.Results: Bacteremia was found in 23(15.3%) out of total 150 young febrile children enrolled in the present study. It shows that in lower YOS score blood culture was sterile and in higher YOS score blood culture was positive for bacteremia, which is statistically significant with p value (<0.05). As per ROC curve analyses the best cut off value of YOS for prediction of bacteremia was 17.5 with sensitivity 91.3%, specificity 81.9%, PPV 47.7% and NPV of 98.1%.Conclusions: YOS of  >17.5 has a good predictive ability for prediction of bacteraemia in young febrile children.

Estimating the area under a receiver operating characteristic curve using partially ordered sets

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ehsan Zamanzade ◽  
Xinlei Wang
Keyword(s):  
Roc Curve ◽  
Characteristic Curve ◽  
Partially Ordered Sets ◽  
Sampling Technique ◽  
Real Data ◽  
Cost Effective ◽  
Ordered Sets ◽  
Operating Characteristics ◽  
Partially Ordered ◽  
Receiver Operating

AbstractRanked set sampling (RSS), known as a cost-effective sampling technique, requires that the ranker gives a complete ranking of the units in each set. Frey (2012) proposed a modification of RSS based on partially ordered sets, referred to as RSS-t in this paper, to allow the ranker to declare ties as much as he/she wishes. We consider the problem of estimating the area under a receiver operating characteristics (ROC) curve using RSS-t samples. The area under the ROC curve (AUC) is commonly used as a measure for the effectiveness of diagnostic markers. We develop six nonparametric estimators of the AUC with/without utilizing tie information based on different approaches. We then compare the estimators using a Monte Carlo simulation and an empirical study with real data from the National Health and Nutrition Examination Survey. The results show that utilizing tie information increases the efficiency of estimating the AUC. Suggestions about when to choose which estimator are also made available to practitioners.

scholarly journals Comparisons of conventional and novel anthropometric obesity indices to predict metabolic syndrome among vegetarians in Malaysia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Kei Ching ◽  
Yit Siew Chin ◽  
Mahenderan Appukutty ◽  
Wan Ying Gan ◽  
Yoke Mun Chan
Keyword(s):  
Roc Curve ◽  
Venous Blood ◽  
Cost Effective ◽  
Shape Index ◽  
Effective Characteristics ◽  
Visceral Adiposity Index ◽  
Fat Percentage ◽  
Male And Female ◽  
Obesity Index ◽  
Obesity Indices

AbstractOur study aimed to compare the ability of anthropometric obesity indices to predict MetS and to determine the sex-specific optimal cut-off values for MetS among Malaysian vegetarians. Body weight, height, waist circumference (WC), blood pressure (BP), fasting venous blood sample were collected from 273 vegetarians in Selangor and Kuala Lumpur, Malaysia. The abilities of body mass index (BMI), body fat percentage (BF%), waist to height ratio (WHtR), lipid accumulation product (LAP), visceral adiposity index (VAI), a body shape index (ABSI), and body roundness index (BRI) to identify MetS were tested using receiver operating characteristic (ROC) curve analyses. MetS was defined according to the Joint Interim Statement 2009. The ROC curve analyses show that BMI, BF%, WHtR, LAP and VAI were able to discriminate MetS in both sexes. LAP was a better predictor to predict MetS, followed by WHtR for male and female vegetarians. The suggested WHtR’s optimal cut-offs and LAP’s optimal cut-offs for MetS for male and female vegetarians were 0.541, 0.532, 41.435 and 21.743, respectively. In conclusion, LAP was a better predictor to predict MetS than other anthropometric obesity indices. However, WHtR could be an alternative obesity index in large epidemiology survey due to its convenient and cost-effective characteristics.

scholarly journals Comparing the Predictive Ability of Prognostic Models in Ischemic Stroke; Derivation, Validation, and Discrimination Beyond the ROC Curve

2014 ◽  
Vol 5 ◽  
Author(s):  
Alireza Esteghamati ◽  
Nima Hafezi-Nejad ◽  
Sara Sheikhbahaei ◽  
Behnam Heidari ◽  
Ali Zandieh ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Roc Curve ◽  
Predictive Ability ◽  
Prognostic Models

scholarly journals Heart Score for Predicting in Hospital Major Cardiovascular Event in Patient with Non ST Segment Elevation Acute Coronary Syndrome

2020 ◽  
Vol 6 (2) ◽  
pp. 85-90
Author(s):  
Kamal Kharrazi Ilyas ◽  
Sutomo Kasiman ◽  
Harris Hasan ◽  
Zulfikri Mukhtar ◽  
Refli Hasan ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
General Hospital ◽  
Cardiovascular Event ◽  
Roc Curve ◽  
Predictive Value ◽  
Predictive Ability ◽  
St Segment Elevation ◽  
Heart Score ◽  
Coronary Syndrome ◽  
St Segment

Background: Acute Coronary Syndrome (ACS) is one of the main problems in the field of cardiovascular diseases because of high hospitalization rate, high mortality and high medical cost. Rapid and accurate risk stratification is needed to calculate the risk of complication and right now exist two most used score which is GRACE and TIMI. Heart score has 5 simple variables that can be calculated easily and this score considered to have better predictive ability compared to other score. The aim of this study is to examine HEART score as a predictor for in hospital Major Cardiovascular Event (MACE) in patient diagnosed as Non ST Segment Elevation Acute Coronary Syndrome (NSTEACS) that hospitalized at Haji Adam Malik (HAM) General Hospital Medan. Methods: This is a prospective cohort study that includes 52 NSTEACS patient that hospitalized at HAM General Hospital since November 2018 until January 2019. Patient that diagnosed as NSTEACS were calculated for GRACE, TIMI, and HEART score then observed during hospitalization. Outcome of this study is MACE during hospitalization. Statistical analysis was performed to test HEART score as MACE predictor and then comparison was done with GRACE and TIMI Results: By using ROC curve analysis, the cut-off value of HEART score was 5 (AUC 0.947, 95% CI 0.883-0.997, p<0.01). Study subject that experienced MACE with HEART score ≥5 was 21 patients (87.5%) compared to 2 patients (7.1%). HEART score ≥5 can predict MACE with sensitivity 87.5%, specificity 92.9%, negative predictive value (NPV) 89.7% and positive predictive value (PPV) 91.3%. ROC curve comparison was done between HEART with GRACE and TIMI then it was found that HEART score has better predictive ability compared to TIMI and GRACE (AUC 0.947 vs 0.829 vs 0.807, p < 0.01). Conclusion: HEART score can be used as MACE predictor which is relatively simpler but have better predictive ability compared to GRACE and TIMI.

scholarly journals Guided endodontics: accuracy of access cavity preparation and discrimination of angular and linear deviation on canal accessing ability—an ex vivo study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yinghui Su ◽  
Chenghui Chen ◽  
Chiahua Lin ◽  
Huina Lee ◽  
Kerkong Chen ◽  
...  
Keyword(s):  
Roc Curve ◽  
Ex Vivo ◽  
Predictive Ability ◽  
Angular Deviation ◽  
Cavity Preparation ◽  
Apical Position ◽  
Anterior Teeth ◽  
Significant Difference ◽  
Different Types ◽  
3D Printed

Abstract Background Guided endodontics technique has been introduced for years, but the accuracy in different types of teeth has yet to be assessed. The aim of this study is to evaluate the accuracy of three dimensional (3D)-printed endodontic guides for access cavity preparation in different types of teeth, and to evaluate the predictive ability of angular and linear deviation on canal accessibility ex vivo. Method Eighty-four extracted human teeth were mounted into six jaw models and categorised into three groups: anterior teeth (AT), premolar (P), and molar (M). Preoperative cone beam computed tomography (CBCT) and surface scans were taken and matched using implant planning software. Virtual access cavity planning was performed, and templates were produced using a 3D printer. After access cavities were performed, the canal accessibility was recorded. Postoperative CBCT scans were superimposed in software. Coronal and apical linear deviations and angular deviations were measured and evaluated with nonparametric statistics. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of angular and linear deviation for canal accessibility in SPSS v20. Results A total of 117 guided access cavities were created and 23 of them were record as canal inaccessibility, but all canals were accessible after canal negotiation. The average linear deviation for all groups was 0.13 ± 0.21 mm at coronal position, 0.46 ± 0.4 mm at apical position, and 2.8 ± 2.6° in angular deviation. At the coronal position, the linear deviations of the AT and P groups were significantly lower than M group deviation (P < 0.05), but no statistically significant difference between AT group and P group. The same results were found in linear deviation at the apical position and in angular deviation. The area under the ROC curve was 0.975 in angular deviation, 0.562 in linear deviation at the coronal position, and 0.786 at the apical position. Statistical significance was noted in linear deviation at the apical position and in angular deviation (P < 0.001). Conclusions In conclusion, this study demonstrated that the accuracy of access cavity preparation with 3D-printed endodontic guides was acceptable. The linear and angular deviations in the M group were significantly higher than those in the other groups, which might be caused by the interference of the opposite teeth. Angular deviation best discriminated the canal access ability of guided access cavity preparation. Graphical Abstract

scholarly journals What are the optimal cut-off points of anthropometric indices for prediction of overweight and obesity? Predictive validity of waist circumference, waist-to-hip and waist-to-height ratios

2020 ◽  
Vol 10 (2) ◽  
pp. 142-147 ◽  
Author(s):  
Helda Tutunchi ◽  
Mehrangiz Ebrahimi-Mameghani ◽  
Alireza Ostadrahimi ◽  
Mohammad Asghari-Jafarabadi
Keyword(s):  
Waist Circumference ◽  
Roc Curve ◽  
Predictive Ability ◽  
Diagnostic Value ◽  
Cross Sectional Study ◽  
Overweight And Obesity ◽  
Roc Curve Analysis ◽  
Anthropometric Indices ◽  
Cross Sectional ◽  
Anthropometric Indicators

Background: Planning for obesity prevention is an important global health priority. Our aim in this study was to find the optimal cut-off points of waist circumference (WC), waist- to- hipratio (WHR) and waist- to- height ratio (WHtR), as three anthropometric indices, for prediction of overweight and obesity. We also aimed to compare the predictive ability of these indices to introduce the best choice. Methods: In this cross-sectional study, a total of 500 subjects were investigated. Anthropometric indicators were measured using a standard protocol. We considered body mass index (BMI) as the simple and most commonly used index for measuring general obesity as the comparison indicator in the present study to assess the diagnostic value for other reported obesity indices.We also performed receiver operating characteristic (ROC) curve analysis to define the optimal cut-off points of the anthropometric indicators and the best indices for overweight and obesity. Results: The proposed optimal cut-offs for WC, WHtR, and WHR were 84 cm, 0.48 and 0.78for women and 98 cm, 0.56 and 0.87 for men, respectively. The area under the ROC curve ofWHtR (women: AUC=0.97, 95% CI: 0.96-0.99 vs. men: AUC=0.97, 95%CI: 0.96-0.99) and WC(women: AUC=0.97, 95% CI, 0.95-0.99 vs. men: AUC=0.98, 95% CI: 0.97-0.99) were greater than WHR (women: AUC=0.79, 95% CI =0.74-0.85 vs. men: AUC=0.84, 95% CI=0.79-0.88). Conclusion: This study demonstrated that the WC and WHtR indicators are stronger indicators compared to the others. However, further studies using desirable and also local cutoffs against more accurate techniques for body fat measurement such as computerized tumor (CT) scans and dual-energy x-ray absorptiometry (DEXA) are required.

scholarly journals Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 928
Author(s):  
Mohammad W. Sahar ◽  
Annabelle Beaver ◽  
Marina A. G. von Keyserlingk ◽  
Daniel M. Weary
Keyword(s):  
Dairy Cattle ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Predictive Value ◽  
Predictive Ability ◽  
Training Dataset ◽  
Test Dataset ◽  
Increased Risk ◽  
High Prevalence ◽  
Random Method

Dairy cattle are particularly susceptible to metritis, hyperketonemia (HYK), and mastitis in the weeks after calving. These high-prevalence transition diseases adversely affect animal welfare, milk production, and profitability. Our aim was to use prepartum behavior to predict which cows have an increased risk of developing these conditions after calving. The behavior of 213 multiparous and 105 primiparous Holsteins was recorded for approximately three weeks before calving by an electronic feeding system. Cows were also monitored for signs of metritis, HYK, and mastitis in the weeks after calving. The data were split using a stratified random method: we used 70% of our data (hereafter referred to as the “training” dataset) to develop the model and the remaining 30% of data (i.e., the “test” dataset) to assess the model’s predictive ability. Separate models were developed for primiparous and multiparous animals. The area under the receiver operating characteristic (ROC) curve using the test dataset for multiparous cows was 0.83, sensitivity and specificity were 73% and 80%, positive predictive value (PPV) was 73%, and negative predictive value (NPV) was 80%. The area under the ROC curve using the test dataset for primiparous cows was 0.86, sensitivity and specificity were 71% and 84%, PPV was 77%, and NPV was 80%. We conclude that prepartum behavior can be used to predict cows at risk of metritis, HYK, and mastitis after calving.

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