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AbstractThe number of novel biomarkers is booming. However, a simple predictive score is more feasible to evaluate the clinical outcome and provide better accuracy. However, the optimal linear combination of correlated biomarkers demands comprehensive methodological research. This research aims to develop a novel approach for interpretable optimization. This research proposes the gradient boost machine with the Youden Index (GBYI) as the target function. The rationale is that the gradient boost machine demonstrates superior prediction ability and provides excellent interpretations according to the linear model. In addition, the Youden Index could effortlessly estimate the optimal cutoff point of the diagnostic test and evaluate the overall accuracy. Simulation studies evaluate the performance of the GBYI with linear and nonlinear structured datasets. We also demonstrate an application in the Bupa Liver Disease Data, which revealed that our optimal combination of correlated biomarkers shows an improved prediction with higher accuracy. This research proposes a novel machine-learning strategy using the powerful statistical boosting technique of the Youden Index. The new machine could optimize the combination of high-dimensional data and provide attractive interpretable coefficients. [ABSTRACT FROM AUTHOR]

Accurate differentiation between health states – diseased or non-diseased – is essential in clinical diagnostics. Optimal cut-off points, or thresholds used to classify test results, are crucial for precise diagnoses. This work introduces the Harmonic Mean of F-score and inverse F-score (HF), a novel metric for a balanced assessment of diagnostic accuracy. HF integrates Specificity (Sp) and Negative Predictive Value (NPV) into the Negative F-score (NFγ), ensuring a comprehensive evaluation of true negatives and negative test reliability. Prioritizing both true positives and true negatives, HF was used in optimal cut-off point estimation under binary disease classification. Simulation results revealed that the HF measure performed well, often surpassing established methods in specific settings. The HF measure and cut-off point selection criterion were applied to real-life data, showcasing its ability to provide a balanced evaluation of diagnostic accuracy. The HF measure frequently outperformed traditional metrics. The HF metric’s flexibility, allowing parameter adjustments to accommodate diverse scenarios, enables researchers and clinicians to tailor its emphasis on specific aspects of diagnostic performance depending on the context. [ABSTRACT FROM AUTHOR]

In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer. [ABSTRACT FROM AUTHOR]

A widely adopted measure of housing affordability is that households should spend no more than 30% of their household income on housing. However, this normative threshold is an arbitrary Great Depression-era guideline and may not be relevant today. This paper proposes a subjective indicator of housing affordability by introducing a method commonly used in the medical sciences. It utilizes discrete information to estimate a subjective affordability ratio that discriminates between subjective house-poor and non-house-poor households. We apply the proposed method to household-level data collected in Selangor, Malaysia, and show that the optimal cut-off point is 23.5%. This estimated value suggests a higher prevalence of house-poor households than is implied by the regularly assumed 30% threshold. In addition, we perform a sensitivity analysis and find the bias in the estimated cut-off point is close to zero. [ABSTRACT FROM AUTHOR]

Purpose: Taiwan, which has a rate of high vehicle ownership, faces significant challenges in managing trauma caused by traffic collisions. In Taiwan, traffic collisions contribute significantly to morbidity and mortality, with a high incidence of severe bleeding trauma. The shock index (SI) and the modified shock index (MSI) have been proposed as early indicators of hemodynamic instability. In this study, we aimed to assess the efficacy of SI and MSI in predicting adverse outcomes in patients with trauma following traffic collisions. Methods: This retrospective cohort study was conducted at Chi Mei Hospital from January 2015 to December 2020. The comprehensive analysis included 662 patients, with data collected on vital signs and outcomes such as mortality, blood transfusion, emergent surgical intervention (ESI), transarterial embolization (TAE), and intensive care unit (ICU) admission. Optimal cutoff points for SI and MSI were identified by calculating the Youden index. Logistic regression analysis was used to assess outcomes, adjusting for demographic and injury severity variables. Results: An SI threshold of 1.11 was associated with an increased risk of mortality, while an SI of 0.84 predicted the need for blood transfusion in the context of traffic collisions. Both SI and MSI demonstrated high predictive power for mortality and blood transfusion, with acceptable accuracy for TAE, ESI, and ICU admission. Logistic regression analyses confirmed the independence of SI and MSI as risk factors for adverse outcomes, thus, providing valuable insights into their clinical utility. Conclusions: SI and MSI are valuable tools for predicting mortality and blood transfusion needs in patients with trauma due to traffic collisions. These findings advance the quality of care for patients with trauma during their transition from the emergency room to the ICU, facilitating prompt and reliable decision-making processes and improving the care of patients with trauma. [ABSTRACT FROM AUTHOR]

The Youden index is often used to measure the effectiveness of biomarkers and aids to find the optimal cutoff point. Since pooled specimens have been shown to be an effective cost-cutting technique, we proposed the exact inferential procedures for the Youden index and its associated cutoff point based on the pooled specimens under the gamma or the inverse Gaussian assumption. The generalized confidence intervals (GCIs) were proposed for the Youden index and its associated cutoff point. Monte Carlo simulations were used to assess the performance of the proposed GCIs. The simulation results show that the proposed GCIs outperformed existing methods such as the bootstrap- p CIs in terms of the coverage probability. Finally, the proposed procedures were illustrated by an example. [ABSTRACT FROM AUTHOR]

ObjectiveNomograms are valuable tools in clinical research for predicting patient outcomes. Understanding threshold values within these models is crucial for assessing the model’s effectiveness and practical application in clinical environments.MethodsWe developed two novel interpretive curves to enhance the utility of nomograms. These curves were designed to provide clear visualization of how clinical prediction models perform across various thresholds. The curves are applied to two case studies to demonstrate their practical application and efficacy.ResultsIn both examples, the novel curves successfully highlighted critical threshold values and revealed changes in prediction accuracy across these thresholds. This enhanced the understanding of the nomogram’s performance, providing clinicians with more informative decision-making tools.ConclusionsThe introduction of these interpretive curves allows for a more nuanced understanding of nomogram-based predictions, offering insights into threshold effects that can inform clinical decisions.

The Youden index is often used to measure the effectiveness of biomarkers and aids to find the optimal cutoff point. Since pooled specimens have been shown to be an effective cost-cutting technique, we proposed the exact inferential procedures for the Youden index and its associated cutoff point based on the pooled specimens under the gamma or the inverse Gaussian assumption. The generalized confidence intervals (GCIs) were proposed for the Youden index and its associated cutoff point. Monte Carlo simulations were used to assess the performance of the proposed GCIs. The simulation results show that the proposed GCIs outperformed existing methods such as the bootstrap-$ p $ CIs in terms of the coverage probability. Finally, the proposed procedures were illustrated by an example.

AbstractThe effectiveness of educational interventions has traditionally been evaluated using effect size measures which focus on a single feature of the distribution of the outcomes under intervention and control conditions: a (standardized) mean difference. Recently there has been increased interest in methods which assess the information contained in the full distributions of outcomes under intervention and control, providing measures of separation from these distributions which do not depend on arbitrary cutoffs, hence which are “threshold-free”. We investigate the statistical relationship between several concepts of this type, and discuss how they can be used to estimate alternative effect size metrics as well as their uncertainties in the context of multilevel models as commonly used for the analysis of educational data. A specific aim of this paper is to investigate how the recently proposed “gain index” relates to other measures of separation including the Area under the Curve (AUC) and the overlapping index. A simulation study, using data with an educationally motivated structure, is presented to compare the different methodologies. [ABSTRACT FROM AUTHOR]

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires. [ABSTRACT FROM AUTHOR]

Abstract Introduction An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. Methods Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. Results Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. Conclusion Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Positive and negative estimates are commonly used by clinicians to evaluate the likelihood of a disease stage being present based on test results. The predicted values are dependent on the prevalence of the underlying illness. However, for certain diseases or clinical conditions, the prevalence is unknown or different from one region to another or from one population to another, leading to an erroneous diagnosis. This article introduces innovative post-test diagnostic precision measures for continuous tests or biomarkers based on the combined areas under the predictive value curves for all possible prevalence values. The proposed measures do not vary as a function of the prevalence of the disease. They can be used to compare different diagnostic tests and/or biomarkers’ abilities for rule-in, rule-out, and overall accuracy based on the combined areas under the predictive value curves. The relationship of the proposed measures to other diagnostic accuracy measures is discussed. We illustrate the proposed measures numerically and use a real data example on breast cancer. [ABSTRACT FROM AUTHOR]

Introduction: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. Methods: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. Results: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. Conclusion: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics. [ABSTRACT FROM AUTHOR]

The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However, the corresponding literature under a three-class setting is limited. In our study, we provide parametric and nonparametric methods that allow investigators to optimally combine biomarkers that seek to discriminate between three classes by minimizing the Euclidean distance from the receiver operating characteristic surface to the perfection corner. Using this Euclidean distance as the objective function allows for estimation of the optimal combination coefficients along with the optimal cutoff values for the combined score. An advantage of the proposed methods is that they can accommodate biomarker data from all three groups simultaneously, as opposed to a pairwise analysis such as the one implied by the three-class Youden index. We illustrate that the derived true classification rates exhibit narrower confidence intervals than those derived from the Youden-based approach under a parametric, flexible parametric, and nonparametric kernel-based framework. We evaluate our approaches through extensive simulations and apply them to real data sets that refer to liver cancer patients. [ABSTRACT FROM AUTHOR]

In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers' accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve (AUC) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining AUC and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this model, we establish the joint asymptotic normality of the maximum empirical likelihood estimator of (AUC,J) and construct an asymptotically valid confidence region for (AUC,J). Furthermore, we propose a new test to determine whether a biomarker simultaneously exceeds prespecified target values of AUC0 and J0 with the null hypothesis H0:AUC≤AUC0 or J≤J0 against the alternative hypothesis Ha:AUC>AUC0 and J>J0. Simulation studies and a real data example on Duchenne Muscular Dystrophy are used to demonstrate the effectiveness of our proposed method and highlight its advantages over existing methods.

The receiver operating characteristic (ROC) curve is a powerful statistical tool and has been widely applied in medical research. In the ROC curve estimation, a commonly used assumption is that larger the biomarker value, greater severity the disease. In this article, we mathematically interpret "greater severity of the disease" as "larger probability of being diseased." This in turn is equivalent to assume the likelihood ratio ordering of the biomarker between the diseased and healthy individuals. With this assumption, we first propose a Bernstein polynomial method to model the distributions of both samples; we then estimate the distributions by the maximum empirical likelihood principle. The ROC curve estimate and the associated summary statistics are obtained subsequently. Theoretically, we establish the asymptotic consistency of our estimators. Via extensive numerical studies, we compare the performance of our method with competitive methods. The application of our method is illustrated by a real‐data example. [ABSTRACT FROM AUTHOR]

Two recession-derivative indicators (RDIs) have been used extensively as forecast objects in business cycle prediction, viz. (1) the target variable takes value 1 if there is a recession starting exactly at a specific horizon in the future, and (2) the target variable takes value 1 if there is a recession starting any time over a specified period in the future. Using daily yield spread as an illustrative predictor, we formally and quantitatively compare the two RDIs using the receiver operating characteristics analysis. Over 1962–2021 covering eight NBER recessions, we find that generally the second RDI, ceteris paribus, will make the the predictor better performing. However, the first RDI can generate better-looking and more useful predictions under certain scenarios, depending on forecast horizon, recession duration and time profile of signals. We also consider a semiannual chronology proposed by Peláez (J Macroecon 45:384–393, 2015) and find that its performance is in the middle of the other two. Our analysis suggests that the choice of a particular RDI should be dictated by the needs of forecast user in a particular decision making context. [ABSTRACT FROM AUTHOR]

Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics. [ABSTRACT FROM AUTHOR]

Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.

Although linearly combining multiple variables can provide adequate diagnostic performance, certain algorithms have the limitation of being computationally demanding when the number of variables is sufficiently high. Liu et al. proposed the min–max approach that linearly combines the minimum and maximum values of biomarkers, which is computationally tractable and has been shown to be optimal in certain scenarios. We developed the Min–Max–Median/IQR algorithm under Youden index optimisation which, although more computationally intensive, is still approachable and includes more information. The aim of this work is to compare the performance of these algorithms with well-known Machine Learning algorithms, namely logistic regression and XGBoost, which have proven to be efficient in various fields of applications, particularly in the health sector. This comparison is performed on a wide range of different scenarios of simulated symmetric or asymmetric data, as well as on real clinical diagnosis data sets. The results provide useful information for binary classification problems of better algorithms in terms of performance depending on the scenario. [ABSTRACT FROM AUTHOR]

Background: Evaluating the performance of SARS-CoV-2 serological assays and clearly articulating the utility of selected antigen, isotypes and thresholds is crucial to understanding the prevalence of infection within selected communities.Methods: This cross-sectional study, implemented in 2020, screened PCR-confirmed COVID-19 patients (n = 86), banked pre-pandemic and negative donors (n = 96), health care workers and family members (n = 552), and university employees (n = 327) for anti-SARS-CoV-2 receptor-binding domain (RBD), trimeric spike protein (S), and nucleocapsid protein (N) IgG and IgA antibodies with a laboratory developed Enzyme-Linked Immunosorbent Assay (ELISA) and tested how antigen, isotype and threshold choices affected the seroprevalence. The following threshold methods were evaluated: (i) mean + 3 standard deviations of the negative controls; (ii) 100% specificity for each antigen/isotype combination; and (iii) the maximal Youden index.Results: We found vastly different seroprevalence estimates depending on selected antigens, isotypes and the applied threshold method, ranging from 0.0% to 85.4% . Subsequently, we maximized specificity and reported a seroprevalence, based on more than one antigen, ranging from 9.3% to 25.9%.Conclusions: This study revealed the importance of evaluating serosurvey tools for antigen, isotype, and threshold-specific sensitivity and specificity, in order to interpret qualitative serosurvey outcomes reliably and consistently across studies. [ABSTRACT FROM AUTHOR]

Background: Due to the high transmissibility of SARS-CoV-2, accurate diagnosis is essential for effective infection control, but the gold standard, real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is costly, slow, and test capacity has at times been insufficient. We compared the accuracy of clinician diagnosis of COVID-19 against RT-PCR in a general adult population. Methods: COVID-19 diagnosis data by 30th September 2021 for participants in an ongoing population-based cohort study of adults in Western Sweden were retrieved from registers, based on positive RT-PCR and clinician diagnosis using recommended ICD-10 codes. We calculated accuracy measures of clinician diagnosis using RT-PCR as reference for all subjects and stratified by age, gender, BMI, and comorbidity collected pre-COVID-19. Results: Of 42,621 subjects, 3,936 (9.2%) and 5705 (13.4%) had had COVID-19 identified by RT-PCR and clinician diagnosis, respectively. Sensitivity and specificity of clinician diagnosis against RT-PCR were 78% (95%CI 77–80%) and 93% (95%CI 93–93%), respectively. Positive predictive value (PPV) was 54% (95%CI 53–55%), while negative predictive value (NPV) was 98% (95%CI 98–98%) and Youden's index 71% (95%CI 70–72%). These estimates were similar between men and women, across age groups, BMI categories, and between patients with and without asthma. However, while specificity, NPV, and Youden's index were similar between patients with and without chronic obstructive pulmonary disease (COPD), sensitivity was slightly higher in patients with (84% [95%CI 74–90%]) than those without (78% [95%CI 77–79%]) COPD. Conclusions: The accuracy of clinician diagnosis for COVID-19 is adequate, regardless of gender, age, BMI, and asthma, and thus can be used for screening purposes to supplement RT-PCR. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is one of the severe causes of death in low-middle-income countries. Being overweight and obese relates to a higher risk of hypertension, which further increases the risk of CVD. Therefore, determining body mass index (BMI) cut-off points is essential to provide a new scale for early and accurate screening. This study aimed to compare three classifications of BMI defined by WHO, Indonesia, and Asian criteria in predicting hypertension in middle-aged Indonesians. We used the 2014 Indonesian Family Life Survey data and included a total sample of 9737 respondents aged 40-60-year-old. We compared values (specificity, sensitivity, negative and positive predictive value, false-positive rate, negative and positive likelihood ratio, Youden index, and prevalence) of three BMI criteria (WHO, Indonesian, and Asian) between groups (Group 1: normal BMI vs overweight + obese BMI; group 2: normal + overweight BMI vs obese BMI) to determine the cut-off points of BMI related to hypertension. The hypertension prevalence was significantly higher in women (48.3%) than in men (42.0%). Respondents' BMI was positively associated with hypertension. The Asian BMI classification showed better sensitivity, specificity, PPV, NPV, FPR, LR+, LR-, and Youden index in group 1 than in group 2. Thus, this study proposed a fitted BMI cut-off point for overweight was =23 kg/m2 and for obesity was =25 kg/m2 as the early screening of overweight and obesity related to hypertension among the middle-aged population in Indonesia. [ABSTRACT FROM AUTHOR]

Spring frost damage (SFD) is the main meteorological disaster limiting the tea industry in the region of the north of the Yangtze River (NYR) in China. Research regarding an SFD indicator for tea plants is of great significance for prevention and control of spring frosts, as well as for timely monitoring and early warning. Based on daily minimum air temperatures (Tmin) and spring frost disaster records in NYR from 1961 to 2020, the Receiver Operating Characteristic curve (ROC) was used to evaluate the accuracy of frost identification, and the critical temperature threshold for SFD to tea plants was determined by jointly comparing Overall Accuracy and the Youden Index. The results showed that the area under the ROC curve was 0.977, indicating that Tmin performed well in identifying frost events. The critical temperature threshold for spring frost in NYR was determined as Tmin of 3.7 °C, and 93 % of frost occurrences were correctly identified. Based on the determined critical threshold of SFD, the number of spring frost days, first frost date (FFD), and last frost date (LFD) in NYR from 1961 to 2020 were analyzed. The results showed that the number of spring frost days in NYR decreased from north to south. FFD appeared earlier and LFD ended later in northern NYR than in southern NYR. These results provide theoretical and technical support for the monitoring and evaluation of spring frosts in NYR, and also for implementing SFD prevention and control measurements for tea plants by use of a critical temperature threshold.

This study aims to redefine obesity cut-off points for body mass index (BMI) and fat mass index (FMI) according to the different age groups of physically active males. Healthy physically active volunteers (N = 1442) aged 18–57 years (y), with a mean BMI = 22.7 ± 2.8 kg/m2, and mean FMI = 4.3 ± 1.7 kg/m2 were recruited from various fitness centers. BMI was calculated and individuals were categorized according to the Asia–Pacific BMI criterion of ≤22.9 kg/m2 and the previous WHO-guided BMI criterion of ≤24.9 kg/m2. FMI was also calculated for the study participants with a cut-off of 6.6 kg/m2. Redefining of BMI and FMI cut-off values was carried out based on different age groups categorized with a difference of 10 y and 5 y using the receiver operating characteristic (ROC) curve and Youden’s index. For the entire study population, BMI redefined cut-off points for overweight and obesity were 23.7 kg/m2 and 24.5 kg/m2, respectively, while FMI redefined cut-off points for overweight and obesity were 4.6 kg/m2 and 5.7 kg/m2, respectively. With 10 y of age group difference, a constant BMI and FMI values were observed, while with 5 y of age group difference, a constant increase in the BMI cut-offs was observed as the age group increased, i.e., from 23.3 kg/m2 in 20–24 y to 26.6 kg/m2 in ≥45 y and a similar trend was seen in FMI cut-offs. To conclude, our study suggests that age-dependent BMI and FMI cut-off points may provide appropriate measurements for physically active males as the age group increases.

It is a common approach to dichotomize a continuous biomarker in clinical setting for the convenience of application. Analytically, results from using a dichotomized biomarker are often more reliable and resistant to outliers, bi-modal and other unknown distributions. There are two commonly used methods for selecting the best cutoff value for dichotomization of a continuous biomarker, using either maximally selected chi-square statistic or a ROC curve, specifically the Youden Index. In this paper, we explained that in many situations, it is inappropriate to use the former. By using the Maximum Absolute Youden Index (MAYI), we demonstrated that the integration of a MAYI and the Kolmogorov–Smirnov test is not only a robust non-parametric method, but also provides more meaningful p value for selecting the cutoff value than using a Mann-Whitney test. In addition, our method can be applied directly in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Hypochromic microcytic red blood cells are common findings in routine laboratory work in all age and ethnic groups. The differentiation between iron deficiency anemia and ß-thalassemia trait represents a challenge in populations with limited access to costly lab investigations Aim: To create a new mathematical formula using RBC hematological parameters to discriminate between iron deficiency anemia and ß-thalassemia trait applicable to Karbala population. Methods: A descriptive cross-sectional study was done at Karbala hereditary blood disease center. The sample of this study was selected from couples attending the premarital screening clinics in Karbala governorate, Iraq. All subjects were adults above 18 years old diagnosed with ß-thalassemia trait or iron deficiency anemia. New formula (Karbala) was developed using binary logistic regression (stepwise backward elimination). The new formula and 28 previously published formulas were evaluated using receiver operator curve ROC analysis. Results: 1380 subjects were included in this study. The new Karbala discriminant formula showed superior performance in ROC curve analysis within Karbala population. Karbala formula has a significantly higher AUC of 0.921 (0.905 - 0.935), followed by England Fraser, MDHL, Matos Carvalho, and Hameed. Conclusion: Hematological discriminant formulas should be tailored to fit the local population of ß-thalassemia trait. Karbala discriminant formula best fits our population. [ABSTRACT FROM AUTHOR]

• EWMA is a PBRTQC tool that provides efficient and continuous monitoring. • Optimization of EWMA features is important for an effective EWMA application. • Youden index provides a low FPR and a high error detection rate for EWMA optimization. • R and similar open-source tools can facilitate implementation phase. This study sets out to investigate the utility of exponentially weighted moving average (EWMA) as patient-based real-time quality control (PBRTQC) by conducting a simulation study and subsequent real-patient data implementation to determine optimal EWMA features (weighting factors, control limits, and truncation methods) based on the Youden index. A simulation experiment was conducted in the first stage to investigate optimal EWMA features for the tests, including aspartate aminotransferase, blood urea nitrogen, and glucose, calcium, creatinine, potassium, sodium, triglycerides, thyroid - stimulating hormone (TSH), and vitamin B12 tests. In the second stage of the study, EWMA was applied to real patient data to elucidate practical utility and achieve final optimal EWMA features. Different degrees of systematic errors (SE) including total allowable error (TEa) as a maximum error level were added to both simulation and patient results, and then the EWMA performance was assessed for different EWMA features. We calculated Youden's index for each combination of EWMA features to find their optimal features to achieve minimum false positive rate (FPR) and maximum error detection rate at the SE level corresponding to TEa. EWMA implementation on real patient data revealed optimal EWMA features for each test. FPR values of creatinine and glucose were 18.48% and 10.17%, respectively, which exceeded the acceptable criteria for FPR (10%). The remaining six analytes showed acceptable FPR. We showed the implementation of EWMA as PBRTQC, and optimization of its features based on the Youden index by conducting extensive performance evaluations and simulations in this study. [ABSTRACT FROM AUTHOR]

Objective: In studies conducted to detect a disease, making a false negative decision in cases such as detecting a deadly disease (Case I), or making a false positive decision in cases where diseases with high treatment costs (Case II) can lead to dangerous results. In this study, a new definition of the area under the curve (AUC) is proposed using a safety threshold value t for the diagnostic test to provide flexible decisions in critical cases. The alternative cut-off point for test diagnosis is evaluated by a simulation study in terms of sensitivity/specificity and relative efficiency. Materials and Methods: A simulation study was performed using different AUC values to obtain the cut-off point c shifted towards c-t for Case I and c+t for Case II. The normal distribution is used for the diseased (X) and non-diseased (Y) data. When obtaining the shift amount t, the gamma probability, which is the desired percentage of increase or decrease in the sensitivity/specificity value, is taken into account. Results: The results of our study showed that the relative efficiency is not significantly affected by working with the safety threshold t value when the test is less accurate and has a low AUC value. Conclusion: In this study, alternative cut-off points are obtained using the shift amount t determined by a predefined gamma probability. It is suggested that in critical situations, using the extra safety threshold t, determining the actual disease margin and safety standards for subjects can provide a more tolerant decision, especially in tests with low discrimination power. [ABSTRACT FROM AUTHOR]

Objective: Convolutional Neural Network(CNN) is increasingly being applied in the diagnosis of gastric cancer. However, the impact of proportion of internal data in the training set on test results has not been sufficiently studied. Here, we constructed an artificial intelligence (AI) system called EGC-YOLOV4 using the YOLO-v4 algorithm to explore the optimal ratio of training set with the power to diagnose early gastric cancer. Design: A total of 22,0918 gastroscopic images from Yixing People's Hospital were collected. 7 training set models were established to identify 4 test sets. Respective sensitivity, specificity, Youden index, accuracy, and corresponding thresholds were tested, and ROC curves were plotted. Results: 1. The EGC-YOLOV4 system completes all tests at an average reading speed of about 15 ms/sheet; 2. The AUC values in training set 1 model were 0.8325, 0.8307, 0.8706, and 0.8279, in training set 2 model were 0.8674, 0.8635, 0.9056, and 0.9249, in training set 3 model were 0.8544, 0.8881, 0.9072, and 0.9237, in training set 4 model were 0.8271, 0.9020, 0.9102, and 0.9316, in training set 5 model were 0.8249, 0.8484, 0.8796, and 0.8931, in training set 6 model were 0.8235, 0.8539, 0.9002, and 0.9051, in training set 7 model were 0.7581, 0.8082, 0.8803, and 0.8763. Conclusion: EGC-YOLOV4 can quickly and accurately identify the early gastric cancer lesions in gastroscopic images, and has good generalization.The proportion of positive and negative samples in the training set will affect the overall diagnostic performance of AI.In this study, the optimal ratio of positive samples to negative samples in the training set is 1:1~ 1:2. [ABSTRACT FROM AUTHOR]

The receiver‐operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time‐to‐event studies, the subject's event status is time‐dependent, and hence, time‐dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right‐censoring and neglect the general case of mixed interval‐censored data that may involve left‐, right‐, and interval‐censored observations. In this context, we propose and study a new time‐dependent ROC curve estimator. We also consider some summary measures (area under the ROC curve and Youden index) traditionally associated with ROC as well as the Youden‐based cutoff estimation method. The proposed method uses available data very efficiently. To this end, the unknown status (positive or negative) of censored subjects are estimated from the data via the estimation of the conditional survival function given the marker. For that, we investigate both model‐based and nonparametric approaches. We also provide variance estimates and confidence intervals using Bootstrap. A simulation study is conducted to investigate the finite sample behavior of the proposed methods and to compare their performance with a competitor. Globally, we observed better finite sample performances for the proposed estimators. Finally, we illustrate the methods using two data sets one from a hypobaric decompression sickness study and the other from an oral health study. The proposed methods are implemented in the R package cenROC. [ABSTRACT FROM AUTHOR]

Determining prerequisite requirements is vital for successful curriculum development and student on-schedule completion of the course of study. This study adapts the Receiver Operating Characteristic (ROC) curve analysis to determine a threshold grade in a prerequisite course necessary for passing the next course in a sequence. This method was tested on a dataset of Calculus 1 and Calculus 2 grades of 164 undergraduate students majoring in mathematics at a private university in Kazakhstan. The results showed that while the currently used practice of setting prerequisite grade requirements is accurately identifying successful completions of Calculus 2, the ROC method is more accurate in identifying possible failures in Calculus 2. The findings also indicate that prior completion of Calculus 1 is positively associated with success in a Calculus 2 course. Thus, this study contributes to the field of mathematics education by providing a new data-driven methodology for determining the optimal threshold grade for mathematics prerequisite courses. [ABSTRACT FROM AUTHOR]

Purpose: Decline in physical function in the early stage after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a major challenge. Exercise tolerance tests, such as the 6-min walk test, are useful markers for predicting exercise tolerance and various other traits, including cardiometabolic risk and non-relapse mortality. This retrospective cohort study aimed to investigate and identify predictors of recovery of exercise tolerance in the early stage after allo-HSCT. Methods: Ninety-eight patients were classified into recovery and non-recovery groups according to the median 6-min walk distance (6MWD) at discharge. Results: Logistic regression analysis revealed that pre-post change in knee extensor strength (ΔKES) and hematopoietic cell transplantation comorbidity index were useful predictors of recovery of exercise tolerance at discharge and moderate predictors of 6MWD recovery in the early post-transplant period. Receiver operating characteristic analysis showed that pre-transplant ΔKES was an accurate predictor of 6MWD recovery in the early post-transplant period. The cutoff point for ΔKES calculated using the Youden index was − 1.17 Nm/kg. Conclusions: The results of this study emphasize the importance of the need for programs designed to prevent muscle weakness in the early period after allo-HSCT. The results from markers of recovery of exercise tolerance are promising and can be used for patient education in rehabilitation programs after allo-HSCT. [ABSTRACT FROM AUTHOR]

Combining multiple biomarkers to provide predictive models with a greater discriminatory ability is a discipline that has received attention in recent years. Choosing the probability threshold that corresponds to the highest combined marker accuracy is key in disease diagnosis. The Youden index is a statistical metric that provides an appropriate synthetic index for diagnostic accuracy and a good criterion for choosing a cut-off point to dichotomize a biomarker. In this study, we present a new stepwise algorithm for linearly combining continuous biomarkers to maximize the Youden index. To investigate the performance of our algorithm, we analyzed a wide range of simulated scenarios and compared its performance with that of five other linear combination methods in the literature (a stepwise approach introduced by Yin and Tian, the min-max approach, logistic regression, a parametric approach under multivariate normality and a non-parametric kernel smoothing approach). The obtained results show that our proposed stepwise approach showed similar results to other algorithms in normal simulated scenarios and outperforms all other algorithms in non-normal simulated scenarios. In scenarios of biomarkers with the same means and a different covariance matrix for the diseased and non-diseased population, the min-max approach outperforms the rest. The methods were also applied on two real datasets (to discriminate Duchenne muscular dystrophy and prostate cancer), whose results also showed a higher predictive ability in our algorithm in the prostate cancer database. [ABSTRACT FROM AUTHOR]

Intelligent predictive systems are showing a greater level of accuracy and effectiveness in early detection of critical diseases like cancer and liver and lung disease. Predictive models assist medical practitioners in identifying the diseases based on symptoms and health indicators like hormones, enzymes, age, bloodcounts, etc. This study proposes a framework to use classification models to accurately detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics techniques. The article proposes an enhanced framework on the original study by Ramana et al. It uses evaluation measures like precision and balanced accuracy to choose the most efficient classification algorithm in India and USA patient datasets using various factors like enzymes, age, etc. Using Youden's Index, individual thresholds for each model were identified to increase the power of sensitivity and specificity. A framework is proposed for highly accurate automated disease detection in the medical industry, and it helps in strategizing preventive measures for patients with liver diseases. [ABSTRACT FROM AUTHOR]

In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor response to a given treatment. The statistical literature is abundant with methods for evaluating single biomarkers for these purposes. However, in practice, a single biomarker rarely captures all aspects of a disease process; therefore, it is often the case that using a combination of biomarkers will improve discriminatory ability. A variety of methods have been developed for combining biomarkers based on the maximization of some global measure or cost-function. These methods usually create a score based on a linear combination of the biomarkers, upon which the standard single biomarker methodologies (such as the Youden's index) are applied. However, these single biomarker methodologies do not account for the multivariable nature of the combined biomarker score. In this article we present generalized inference and bootstrap approaches to estimating confidence intervals for the Youden's index and corresponding cut-point for a combined biomarker. These methods account for inherent dependencies and provide accurate and efficient estimates. A simulation study and real-world example utilize data from a Duchene Muscular Dystrophy study are also presented. [ABSTRACT FROM AUTHOR]

It is common to compare biomarkers' diagnostic or prognostic performance using some summary ROC measures such as the area under the ROC curve (AUC) or the Youden index. We propose to compare two paired biomarkers using both the AUC and the Youden index since the two indices describe different aspects of the ROC curve. This comparison can be made by estimating the joint confidence region (an elliptical area) of the differences of the paired AUCs and the Youden indices. Furthermore, for deciding if one marker is better than the other in terms of both the AUC and the Youden index (J), we can test H0:AUCa≤AUCb or Ja≤Jb against Ha:AUCa>AUCb and Ja>Jb using the paired differences. The construction of such a joint hypothesis is an example of the multivariate order‐restricted hypotheses. For such a hypothesis, we propose and compare three testing procedures: (1) the intersection‐union test (IUT); (2) the conditional test; and (3) the joint test. The performance of the proposed inference methods was evaluated and compared through simulations. The simulation results demonstrate that the proposed joint confidence region maintains the desired confidence level, and all three tests maintain the type I error under the null. Furthermore, among the three proposed testing methods, the conditional test is the preferred approach with markedly larger power consistently than the other two competing methods. [ABSTRACT FROM AUTHOR]

Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non‐malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi‐step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut‐point selection methods with great computational ease. The performance of proposed measures and cut‐point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut‐points were estimated for the risk of ovarian malignancy algorithm score. [ABSTRACT FROM AUTHOR]

For evaluating diagnostic accuracy of inherently continuous diagnostic tests/biomarkers, sensitivity and specificity are well‐known measures both of which depend on a diagnostic cut‐off, which is usually estimated. Sensitivity (specificity) is the conditional probability of testing positive (negative) given the true disease status. However, a more relevant question is "what is the probability of having (not having) a disease if a test is positive (negative)?". Such post‐test probabilities are denoted as positive predictive value (PPV) and negative predictive value (NPV). The PPV and NPV at the same estimated cut‐off are correlated, hence it is desirable to make the joint inference on PPV and NPV to account for such correlation. Existing inference methods for PPV and NPV focus on the individual confidence intervals and they were developed under binomial distribution assuming binary instead of continuous test results. Several approaches are proposed to estimate the joint confidence region as well as the individual confidence intervals of PPV and NPV. Simulation results indicate the proposed approaches perform well with satisfactory coverage probabilities for normal and non‐normal data and, additionally, outperform existing methods with improved coverage as well as narrower confidence intervals for PPV and NPV. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set is used to illustrate the proposed approaches and compare them with the existing methods. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer with a 5‐year survival rate of less than 5%. As in many other diseases, its diagnosis might involve progressive stages. It is common that in biomarker studies referring to PDAC, recruitment involves three groups: healthy individuals, patients that suffer from chronic pancreatitis, and PDAC patients. Early detection and accurate classification of the state of the disease are crucial for patients' successful treatment. ROC analysis is the most popular way to evaluate the performance of a biomarker and the Youden index is commonly employed for cutoff derivation. The so‐called generalized Youden index has a drawback in the three‐class case of not accommodating the full data set when estimating the optimal cutoffs. In this article, we explore the use of the Euclidean distance of the ROC to the perfection corner for the derivation of cutoffs in trichotomous settings. We construct an inferential framework that involves both parametric and nonparametric techniques. Our methods can accommodate the full information of a given data set and thus provide more accurate estimates in terms of the decision‐making cutoffs compared with a Youden‐based strategy. We evaluate our approaches through extensive simulations and illustrate them on a PDAC biomarker study. [ABSTRACT FROM AUTHOR]