Joint hypothesis testing of the area under the receiver operating characteristic curve and the Youden index

2021 ◽  
Author(s):  
Jingjing Yin ◽  
Fedelis Mutiso ◽  
Lili Tian
Keyword(s):  
Hypothesis Testing ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Youden Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

scholarly journals Geriatric Health Charts for Individual Assessment and Prediction of Care Needs: A Population-Based Prospective Study

2018 ◽  
Vol 75 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Giola Santoni ◽  
Amaia Calderón-Larrañaga ◽  
Davide L Vetrano ◽  
Anna-Karin Welmer ◽  
Nicola Orsini ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
P Value ◽  
Care Needs ◽  
Social Care Service ◽  
Health Changes ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Background Geriatric health charts that are similar to pediatric growth charts could facilitate monitoring health changes and predicting care needs in older adults. We aimed to validate an existing composite score (Health Assessment Tool [HAT]) and provide provisional age-specific reference curves for the general older population. Methods Data came from the Swedish National study on Aging and Care in Kungsholmen (N = 3,363 participants aged 60 years and over examined clinically at baseline and 3 years later). HAT was validated by exploring its relationship with health indicators (logistic regression) and comparing its ability to predict care consumption with that of two of its components, morbidity and disability (receiver operating characteristic curve areas). A flowchart was developed to obtain individual-level HAT scores (nominal response method). Sex-specific health charts were derived by graphing seven percentile curves of age-related HAT change (logistic quantile regression). Results HAT scores above the age- and sex-specific median were related to good performance in chair-stand tests (odds ratio [OR] = 2.62, 95% confidence interval [CI]: 2.07–3.31), balance and grip tests (interaction balance grip test, OR = 1.15, 95% CI: 1.05–1.25), and good self-rated health (OR = 2.19, 95% CI: 1.77–2.71). Receiver operating characteristic curve areas (HAT vs number of chronic disorders) were formal care, 0.76 versus 0.58 (p value &lt; .001); informal care, 0.74 versus 0.59 (p value &lt; .001); hospital admission, 0.70 versus 0.66 (p value &lt; .001); primary care visits, 0.71 versus 0.69 (p value &gt; .05); and specialty care visits, 0.62 versus 0.65 (p value &lt; .001). HAT consistently predicted medical and social care service use better than disability. Conclusions HAT is a valid tool that predicts care consumption well and could be useful in developing geriatric health charts to better monitor health changes in older populations.

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