scholarly journals Perspective Biological Markers for Autism Spectrum Disorders: Advantages of the Use of Receiver Operating Characteristic Curves in Evaluating Marker Sensitivity and Specificity

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Provvidenza M. Abruzzo ◽  
Alessandro Ghezzo ◽  
Alessandra Bolotta ◽  
Carla Ferreri ◽  
Renato Minguzzi ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Receiver Operating Characteristic ◽  
Biological Markers ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Metabolic Diseases ◽  
Autism Spectrum ◽  
Published Data ◽  
Spectrum Disorders ◽  
Receiver Operating

Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental disorders. Recognized causes of ASD include genetic factors, metabolic diseases, toxic and environmental factors, and a combination of these. Available tests fail to recognize genetic abnormalities in about 70% of ASD children, where diagnosis is solely based on behavioral signs and symptoms, which are difficult to evaluate in very young children. Although it is advisable that specific psychotherapeutic and pedagogic interventions are initiated as early as possible, early diagnosis is hampered by the lack of nongenetic specific biological markers. In the past ten years, the scientific literature has reported dozens of neurophysiological and biochemical alterations in ASD children; however no real biomarker has emerged. Such literature is here reviewed in the light of Receiver Operating Characteristic (ROC) analysis, a very valuable statistical tool, which evaluates the sensitivity and the specificity of biomarkers to be used in diagnostic decision making. We also apply ROC analysis to some of our previously published data and discuss the increased diagnostic value of combining more variables in one ROC curve analysis. We also discuss the use of biomarkers as a tool for advancing our understanding of nonsyndromic ASD.

scholarly journals Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Li-Yeh Chuang ◽  
Shyng-Shiou F. Yuan ◽  
Ming-Feng Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Growth Factor Receptor ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Prognostic Indicators ◽  
Clinicopathologic Characteristics ◽  
Receiver Operating

The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.

scholarly journals Bayesian Sensitivity-Specificity and ROC Analysis for Finding Key Drivers

2021 ◽  
Vol 19 (1) ◽  
pp. 2-15
Author(s):  
Stan Lipovetsky ◽  
Michael W. Conklin
Keyword(s):  
Receiver Operating Characteristic ◽  
Statistical Modeling ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Regression Modeling ◽  
Modeling And Analysis ◽  
Numerical Comparisons ◽  
Key Drivers ◽  
Sensitivity Specificity ◽  
Receiver Operating

Finding key drivers in regression modeling via Bayesian Sensitivity-Specificity and Receiver Operating Characteristic is suggested, and clearly interpretable results are obtained. Numerical comparisons with other techniques show that this methodology can be useful in practical statistical modeling and analysis helping to researchers and managers in making meaningful decisions.

Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

2016 ◽  
Author(s):  
T. K. Patbandha ◽  
K. Ravikala ◽  
B. R. Maharana ◽  
Rupal Pathak ◽  
S. Marandi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Positive Likelihood Ratio ◽  
Threshold Values ◽  
Characteristic Analysis ◽  
Statistical Tool ◽  
Milk Samples ◽  
Diagnostic Indicator ◽  
Receiver Operating

Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer ‘LACTOSCAN’ and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.

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