Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

Ünal, İlker

doi:10.1155/2017/3762651

Cited by 492 publications

(355 citation statements)

References 14 publications

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“…We calculated YI by deducting 1 from the sum of the test’s SN and SP expressed not as a percentage but as part of a whole number: (SN + SP) − 1. It is one of the oldest measures for diagnostic accuracy, being used for the evaluation of overall discriminative power of a diagnostic procedure and for comparison of this test with other tests ( 22 ) . For a test with poor diagnostic accuracy, YI equals 0, and in a perfect test YI equals 1.…”

Section: Methodsologymentioning

confidence: 99%

Mid-upper arm circumference cut-offs for screening thinness and severe thinness in Indian adolescent girls aged 10–19 years in field settings

Sethi¹,

Gupta²,

Pedgaonkar³

et al. 2019

Public Health Nutr.

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Objective:(i) To assess diagnostic accuracy of mid-upper arm circumference (MUAC) for screening thinness and severe thinness in Indian adolescent girls aged 10–14 and 15–19 years compared with BMI-for-age Z-score (BAZ) <−2 and <−3 as the gold standard and (ii) to identify appropriate MUAC cut-offs for screening thinness and severe thinness in Indian girls aged 10–14 and 15–19 years.Design:Cross-sectional, conducted October 2016–April 2017.Setting:Four tribal blocks of two eastern India states, Chhattisgarh and Odisha.Participants:Girls (n 4628) aged 10–19 years. Measurements included height, weight and MUAC to calculate BAZ. Standard diagnostic accuracy tests, receiver–operating characteristic curves and Youden index helped arrive at MUAC cut-offs at BAZ < −2 and <−3, as gold standard.Results:Mean MUAC and BMI correlation was positive (0·78, P = 0·001 and r 2 = 0·61). Among 10–14 years, MUAC cut-off corresponding to BAZ < −2 and BAZ < −3 was ≤19·4 and ≤18·9 cm. Among 15–19 years, corresponding values were ≤21·6 and ≤20·7 cm. For both BAZ < −2 and BAZ < −3, specificity was higher in 15–19 v. 10–14 years. State-wise variations existed. MUAC cut-offs ranged from 17·7 cm (10 years) to 22·5 cm (19 years) for BAZ < −2, and from 17·0 cm (10 years) to 21·5 cm (19 years) for BAZ < −3. Single-age area under the curve range was 0·82–0·97.Conclusions:Study provides a case for use of year-wise and sex-wise context-specific MUAC-cut-offs for screening thinness/severe thinness in adolescents, rather than one MUAC cut-off across 10–19 years, depending on purpose and logistic constraints.

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Section: Methodsologymentioning

confidence: 99%

Mid-upper arm circumference cut-offs for screening thinness and severe thinness in Indian adolescent girls aged 10–19 years in field settings

Sethi¹,

Gupta²,

Pedgaonkar³

et al. 2019

Public Health Nutr.

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show abstract

“…This statistical technique generates optimal threshold cutoffs by maximizing the sensitivity and specificity for each respective variable, which in practice involves finding the greatest vertical distance between the ROC curve and the diagonal chance line . We used Youden's J statistic because it is a clinically relevant method . Subjects with scores below the cutoff were categorized as low‐risk nonadherent patients, and those with scores above the cutoff were categorized as high‐risk nonadherent patients.…”

Section: Methodsmentioning

confidence: 99%

Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool

Desai

Marlow

2019

Pain Practice

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Objective To develop and validate a risk assessment tool called the Prescription Medication Non‐Adherence Prediction Tool (Rx‐NAPT) to predict medication nonadherence in patients with fibromyalgia. Methods This was a retrospective cohort study using claims data from South Carolina Medicaid. Patients with fibromyalgia who were ≥18 years old and who had filled at least 1 prescription medication for pregabalin, duloxetine, or milnacipran from January 1, 2005, through June 30, 2011 were included. Medication possession ratios (MPRs) were calculated to classify patients as adherent (MPR ≥ 80%) or nonadherent (MPR < 80%). Multivariable logistic models using 100 bootstrap replications (with replacement) were used to identify factors associated with medication nonadherence, including age, gender, race, days’ supply, medication type, and fibromyalgia‐related comorbidity score. Weighted β coefficients of the predictors were used to create the Rx‐NAPT. Youden's J statistic was used to classify nonadherent patients into different levels of risk. Results The study sample comprised 6,626 patients with fibromyalgia, where 4,804 (72.50%) were non‐adherent and 1,822 (27.50%) were adherent to their prescribed medication(s). Logistic regression models showed that 7 predictors (gender, age, race, fibromyalgia‐related comorbidity score, medication type, health maintenance organization coverage, emergency room visit) were statistically significant in ≥50% of the bootstrapped samples. The final model demonstrated reasonable discrimination (area under the curve [AUC] = 0.6224) and calibration (Hosmer‐Lemeshow goodness‐of‐fit; P > 0.05) statistics and was validated internally (AUC = 0.6372). Conclusion Poor adherence with medication remains an important barrier to providing optimal care. Our risk prediction model provides an easy tool to help clinicians better identify patients with fibromyalgia who may not take their medications as prescribed.

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“…AUCs were calculated using the pROC, version 1.15, package. In the same 5‐year follow‐up subcohorts, we applied Youden's method to select the optimal cut point in the cognitive symptom burden score for identification of 5‐year dementia risk and then calculated sensitivity and specificity based on this cut point . Cut point analysis used cutpointr, version 0.7.6.…”

Section: Methodsmentioning

confidence: 99%

Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

McCoy

Han

Pellegrini

et al. 2020

Alzheimer's & Dementia

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Introduction Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high‐risk individuals. Methods We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow‐up. Results Among 267,855 hospitalized patients with 1,251,858 patient years of follow‐up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54–1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients. Discussion A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.

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Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

Cited by 492 publications

References 14 publications

Mid-upper arm circumference cut-offs for screening thinness and severe thinness in Indian adolescent girls aged 10–19 years in field settings

Mid-upper arm circumference cut-offs for screening thinness and severe thinness in Indian adolescent girls aged 10–19 years in field settings

Risk for Medication Nonadherence Among Medicaid Enrollees With Fibromyalgia: Development of a Validated Risk Prediction Tool

Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

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