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The problem that often comes in poultry industry is the process of composition selection of poultry feed that is not efficient. Feed ingredients with high nutrients have a relatively expensive price while low-cost feeds contain inadequate nutritional needs of livestock. Breeders should reduce the cost of spending to generate higher income. These problems can be solved using genetic algorithms to obtain the composition of feed ingredients in accordance with the needs of livestock nutrition and have a minimum price. The simple genetic algorithm applied takes a long time to reach the optimal solution. Therefore, genetic operator should be improved to increase the ability of genetic algorithm in finding the optimal solution. This study uses a combination of different crossover and mutation methods. We propose extended intermediate and one cut point method in the crossover process. We use two method of mutation which are random and inverse mutation. The experiment result shows that the optimum population size is 250, the best combination of cr and mr of this experiment is 0.7 and 0.3 and the number of generations is 1000. According to the result, the experiment proves that adaptive genetic algorithm is efficient in solving this kind of problems.

The 2018 NIA-AA research framework proposes a classification system with beta-Amyloid deposition, pathologic Tau, and neurodegeneration (ATN) for the diagnosis and staging of Alzheimer’s Disease (AD). Data from the ADNI (AD neuroimaging initiative) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI) and AD subjects followed over a 3-10 year period were measured via multiple reaction monitoring (MRM) mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181) and hippocampal volume were also measured. From these candidate markers, optimal diagnostic signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume and a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting this signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used marker, and is much easier to use in practice than recently published complex signatures. In addition, this signature reinforces the ATN construct from the 2018 NIA-AA research framework.DisclosuresViswanath Devanarayan is an employee of Charles River Laboratories, and as such owns equity in, receives salary and other compensation from Charles River Laboratories.Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Background Three dimensional (3D) markerless asymmetry analysis was developed to assess and monitor the scoliotic curve. While the developed surface topography (ST) indices demonstrated a strong correlation with the Cobb angle and its change over time, it was reported that the method requires an expert for monitoring the procedure to prevent misclassification for some patients. Therefore, this study aimed at improving the user-independence level of the previously developed 3D markerless asymmetry analysis implementing a new asymmetry threshold without compromising its accuracy in identifying the progressive scoliotic curves. Methods A retrospective study was conducted on 128 patients with Adolescent Idiopathic Scoliosis (AIS), with baseline and follow-up radiograph and surface topography assessments. The suggested “cut point” which was used to separate the deformed surfaces of the torso from the undeformed regions, automatically generated deviation patches corresponding to scoliotic curves for all analyzed surface topography scans. Results By changing the “cut point” in the asymmetry analysis for monitoring scoliotic curves progression, the sensitivity for identifying curve progression was increased from 68 to 75%, while the specificity was decreased from 74 to 59%, compared with the original method with different “cut point”. Conclusions These results lead to a more conservative approach in monitoring of scoliotic curves in clinical applications; smaller number of radiographs would be saved, however the risk of having non-measured curves with progression would be decreased.

OBJECTIVES: We report the outcomes of patients with squamous cell carcinoma of the head and neck (HNSCC) whose circulating tumor cells (CTCs) were quantified using surface-enhanced Raman scattering (SERS) nanotechnology. METHODS: SERS tagged with EGF was used to directly measure targeted CTCs. Patient charts were retrospectively reviewed. An optimal cut point for CTCs in 7.5 ml of peripheral blood predictive of for distant metastasis-free survival (DMFS) was identified by maximizing the log-rank statistic. An ROC analysis was also performed. RESULTS: Of 82 patients, 13 experienced metastatic progression. The optimal cut point for DMFS was 675 CTCs (p = 0.047). For those with distant recurrence (n = 13) versus those without distant recurrence (n = 69), the CTC cut point which results in the largest combined sensitivity and specificity values is also 675 (sensitivity = 69%, specificity = 68%). CONCLUSION: Liquid biopsy techniques in HNSCC show promise as a means of identifying patients at greater risk of disease progression.

Cut point determination is an important aspect of immunogenicity assay development. The cut point can be influenced by a myriad of factors. Key among those is the analytical variability of the assay itself and biological variation due to test samples. Since a smaller cut point value may result in improved sensitivity, the existing procedures often employ statistical techniques such as outlier removal to produce a conservative cut point. Although such practices are intended to yield acceptable assay sensitivity, they may fail to fully account for biological variability in the data, thus generating higher than expected number of false positive results. In this paper, we introduce the concept of minimum cut point. It is defined as the cut point that is determined in the absence of biological variability. Under the log-normal assumption of the data used for cut point analysis, closed-form formulas are derived for the minimum cut point. This minimum cut point can be used to benchmark whether a cut point derived from a procedure can compromise assay specificity by being too low.

In Japanese patients with non-muscle-invasive urothelial carcinoma of the bladder, the impact of body mass index (BMI) on recurrence following transurethral resection of bladder tumor (TURBT) is unclear. The present study retrospectively examined data collected from 50 patients diagnosed with primary urothelial carcinoma of the bladder (pTa, pTis, and pT1) who had previously undergone TURBT surgery. Two BMI cut-off points for predicting disease recurrence were evaluated: i) A threshold generated through receiver operating characteristic (ROC) curve analysis; ii) the World Health Organization BMI index (24 kg/m2) for overweight status in Japanese populations. Univariate and multivariate analyses were applied to assess individual variables (BMI included) and the effect they had on recurrence-free survival (RFS). Median RFS and BMI values of 19.72 months (range, 3.13–72.13 months) and 23.37 kg/m2 (range, 14.72–36.84 kg/m2), respectively, were recorded. In multivariate analyses, higher continuous BMI was significantly associated with shorter RFS (P=0.019). Based on a ROC-generated BMI cut-off point (23.4 kg/m2), patients were ranked with either a high (≥23.4 kg/m2) or low (23.4 kg/m2 were significantly associated with shorter RFS (P=0.028). Intravesical Bacillus Calmette-Guérin treatment and history of upper-tract urothelial carcinoma were also independently associated (P=0.044 and P=0.010, respectively). However, BMI values >24 kg/m2 (customary cut-off point) had no significant impact on RFS (P=0.066). Thus, a higher BMI status was revealed to be independently predictive of shorter RFS in Japanese patients undergoing TURBT for urothelial carcinoma of the bladder. A greater number of samples are required in order to determine optimal BMI cut-off points in Japanese patients and to investigate whether weight reduction intervention may improve prognosis.

Background Left ventricular diastolic dysfunction ( DD ) is common, particularly in women and older individuals, and it is associated with adverse cardiovascular outcomes. We evaluated the impact of age‐ and sex‐specific diagnostic criteria on the assessment of DD in the community‐based Framingham Heart Study. Methods and Results We estimated age‐ and sex‐specific reference limits for echocardiographic measures of DD in a healthy reference subsample (N=2355, mean age 44 years, 66% women). The prevalence, correlates, and association with future cardiovascular disease were compared for DD using age‐ and sex‐specific versus single cut point reference limits in a broad sample (N=6102, mean age 50 years, 56% women). Using age‐ and sex‐specific criteria, DD was present in ≈25% to 30% of individuals across age groups, and it was directly associated with a number of modifiable risk factors. In contrast, with single cut point criteria, age was the primary determinant of DD . During follow‐up (mean 7.9±2.2 years), incident cardiovascular disease occurred in 213 of 5770 individuals. Using age‐ and sex‐specific criteria, mild and moderate‐severe DD were associated with 50% (95% confidence interval, 1.09–2.05) and 65% (95% confidence interval, 1.14–2.38) higher incidences of cardiovascular disease, respectively, in age‐ and sex‐adjusted analyses. With single cut point criteria, moderate‐severe DD (hazard ratio, 1.66; 95% confidence interval, 1.05–2.61), but not mild DD (hazard ratio, 0.94; 95% confidence interval, 0.63–1.40), was associated with incident cardiovascular disease. Conclusions Age‐ and sex‐specific reference limits may result in DD assessments that are less dependent on age, more robustly related to modifiable risk factors, and are more closely associated with incident cardiovascular disease.

The German version of the Social Phobia and Anxiety Inventory (SPAI-G) is a validated measure for the detection of social anxiety disorder (SAD). The aim of the present study was to develop optimal cut points (OC) for remission and response to treatment for the SPAI-G.We used Receiver Operating Characteristic methods and bootstrapping to analyse the data of 359 patients after psychotherapeutic treatment. OCs where defined as the cut points with the highest sensitivity and specificity after bootstrapping.For remission, an OC of 2.79 was found, and for response, a change in score from pre- to posttreatment by 11% yielded best results.The OC we identified for remissionmay be used to improve the diagnostic utility of the SPAI-G. However, the cut point for response achieved only borderline-acceptable levels of sensitivity and specificity, calling into doubt their utility in clinical and research setting.Zusammenfassung Optimale Cut-Points für Remission und Response für die deutsche Version des Social Phobia and Anxiety Inventory (SPAI) Fragestellung: Die deutsche Version des Social Phobia and Anxiety Inventory (SPAI-G) ist ein validiertes Maß bei der Erkennung von Sozialer Phobie. Das Ziel der vorliegenden Studie war die Entwicklung optimaler Cut-Points (OC) für Remission und Response to treatment für das SPAI-G. Methode: Wir verwendeten Receiver Operating Characterstic Methoden und Bootstrapping, um die Daten von 359 Patienten nach Beendigung einer psychotherapeutischen Behandlung zu untersuchen. Ergebnisse: Als optimaler Cut-Point wurde der Cut-Point mit der höchsten Sensitivität und Spezifität definiert. Für Remission erreichte ein optimaler Cut-Point von 2.79 und für Response eine Prä-Post-Veränderung von 11% die besten Ergebnisse. Diskussion: Der von uns identifizierte optimale Cut-Point für Remission kann zur Verbesserung des diagnostischen Nutzens der SPAI-G genutzt werden. Der Cut-Point für Response erreichte allerdings nur grenzwertige Sensitivität und Spezifität, weshalb sein Nutzen für klinische und wissenschaftliche Zwecke zweifelhaft ist.

Histomorphologic prognostic biomarkers that can be measured using only an hematoxylin and eosin stain are very attractive because they are simple and cheap. We conceived an entirely novel biomarker of this type, the Breslow density (BD), which measures invasive melanoma cell density at the site where Breslow thickness (BT) is measured. This study assessed BD's prognostic value. In this study, BD was measured in 1329 melanoma patients. Measurement accuracy and precision was assessed using intraclass correlation coefficient (ICC). Survival was assessed with a primary end-point of melanoma-specific survival (MSS) and also overall survival and metastasis-free survival. We found that BD measurement was accurate compared with gold standard image analysis (ICC, 0.84). Precision was excellent for 3 observers with different experience (ICC, 0.93) and for an observer using only written instructions (ICC, 0.93). BD was a highly significant predictor in multivariable analysis for overall survival, MSS, and metastasis-free survival (each, P

Purpose (1) to examine the ability of the Orebro Musculoskeletal Pain Screening Questionnaire-short version (OMPSQ-SF) to predict time to return to pre-injury work duties (PID) following a work-related soft tissue injury (regardless of body location); and (2) to examine the appropriateness of 50/100 as a suitable cut-off score for case identification. Methods Injured workers (IW) from six public hospitals in Sydney, Australia, who had taken medically-sanctioned time off work due to their injury, were recruited by insurance case managers within 5-15 days of their injury. Eligible participants (N = 213 in total) were administered the OMPSQ-SF over the telephone by the case manager. For objective (1) Cox proportional hazards regression analysis was used to predict days to return to PID using the OMPSQ-SF. For objective (2) receiver operator characteristic (ROC) analysis was used to determine the OMPSQ-SF total score that optimises sensitivity and specificity in detecting whether or not participants had returned to PID within 2-7 weeks. Results The total OMPSQ-SF score significantly predicted number of days to return to PID, such that for every 1-point increase in the total OMPSQ-SF score the predicted chance of returning to work reduced by 4% (i.e., hazard ratio = 0.96), p

Background The appropriate limit to the amount of daily sedentary time (ST) required to minimize mortality is uncertain. This meta-analysis aimed to quantify the dose-response association between daily ST and all-cause mortality and to explore the cut-off point above which health is impaired in adults aged 18–64 years old. We also examined whether there are differences between studies using self-report ST and those with device-based ST. Methods Prospective cohort studies providing effect estimates of daily ST (exposure) on all-cause mortality (outcome) were identified via MEDLINE, PubMed, Scopus, Web of Science, and Google Scholar databases until January 2018. Dose-response relationships between daily ST and all-cause mortality were examined using random-effects meta-regression models. Results Based on the pooled data for more than 1 million participants from 19 studies, the results showed a log-linear dose-response association between daily ST and all-cause mortality. Overall, more time spent in sedentary behaviors is associated with increased mortality risks. However, the method of measuring ST moderated the association between daily ST and mortality risk (p

PURPOSE: This study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity. METHODS: Fifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures. RESULTS: All cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%). CONCLUSION: Wrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.

The problem of cost is increasingly becoming the hotspot of academic research. Therefore, it is necessary to take the cost into account in the research of decision tree algorithm. In this paper, a new algorithm based on adaptive mechanism was proposed. The heuristic function was improved to solve the high cost of the existing cost-sensitive decision tree problem and the multi-valued attribute bias problem. The adaptive determing parameters mechanism, adaptive selecting the cut point mechanism and adaptive removing attribute mechanism were all applied to the process of tree building to improve the model building efficiency. Finally, the improved "probability refusing pruning" strategy was applied to the model of decision tree. Experiments showed that the efficiency of the new algorithm was improved obviously on large data set, and it can also be used to build a model of decision tree with low redundancy and low cost.

OBJECTIVES This study aimed to develop and validate a falls risk screening tool derived from interRAI Acute Care (AC) Assessment. METHODS For derivation and validation, two prospective cohorts were recruited from AC hospitals in Australia. The derivation cohort comprised 1418 patients from 11 hospitals. In the validation cohort, 393 patients were recruited from four hospitals. The interRAI AC tool was used to collect comprehensive geriatric assessment data at admission. In-hospital falls were documented from medical records. A falls risk score was calculated using logistic regression. Predictive ability was compared with St. Thomas Risk Assessment Tool In Falling elderlY (STRATIFY), using area under curve (AUC). The validation cohort provided external validity. RESULTS Complete data in the derivation cohort were available for 1288 patients (91%), with 75 (5.8%) having an in-hospital fall. The derived interRAI AC falls risk score (range = 0-6) had significantly better predictive ability (AUC = 0.70, 95% confidence interval [CI] = 0.63-0.76) compared with St. Thomas Risk Assessment Tool In Falling elderlY (AUC = 0.64, 95% CI = 0.58-0.70) (P = 0.033). At a cut point of three, 54 of 75 falls were correctly predicted by the falls risk score derived from interRAI AC (sensitivity = 0.72 [95% CI = 0.60-0.82] and specificity = 0.60 [95% CI = 0.57-0.62]). The falls risk score performed similarly in the validation cohort. CONCLUSIONS The falls risk tool developed from interRAI AC is a valid measure to screen for in-hospital falls. Reduction in assessment burden without loss of fidelity can be achieved through integrating the risk screener within the interRAI hospital system, which automatically triggers protocols for falls prevention based on identified risk.

In this chapter we consider classes of binomial ideals which are naturally attached to finite simple graphs. The first of these classes are the binomial edge ideals. These ideals may also be viewed as ideals generated by a subset of 2-minors of a (2 × n)-matrix of indeterminates. Their Grobner bases will be computed. Graphs whose binomial edge ideals have a quadratic Grobner basis are called closed graphs. A full classification of closed graphs is given. For an arbitrary graph the initial ideal of the binomial edge ideal (for a suitable monomial order) is a squarefree monomial ideal. This has the pleasant consequence that the binomial edge ideal itself is a radical ideal. Its minimal prime ideals are determined in terms of cut point properties of the underlying graph. Based on this information, the closed graphs whose binomial edge ideal is Cohen–Macaulay are classified. In the subsequent sections, the resolution of binomial edge ideals is considered and a bound for the Castelnuovo–Mumford regularity of these ideals is given. Finally, the Koszul property of binomial edge ideals is studied. Intimately related to binomial edge ideals are permanental edge ideals and Lovasz, Saks, and Schrijver edge ideals. Their primary decomposition will be studied.

The objective of this study is to derive a simple, yet effective type 2 Diabetes Risk Score Tool for Indian population using semantic discretization and machine learning techniques. The dataset used for training and validation is taken from Annual Health Survey, containing over 1.65 million people’s health-related information from 284 districts of India. This is the first study of its kind that truly represents the Indian population. A combination of feature selections techniques is used to find the minimal subset of attributes that optimally contribute in determining the class attribute. Continuous independent variables (various diabetes risk factors) are discretized using semantic discretization technique. The discretized dataset is then used in deriving Weighted Diabetes Risk Score for each risk factor. An optimal cutoff value for Total Weighted Diabetes Risk Score (TWDRS) is determined based on the evaluation parameters such as sensitivity, specificity, prediction accuracy, and proportion of population kept in high risk. The dataset used for this study contains 16,38,923 records. Records (7,42,605) that meet our criteria are selected for this study. Experimental results show that, at optimal cut point, TWDRS >=19, sensitivity is 72.55%, specificity is 61.99%, and proportion of population at high risk is 39.29%.

Antiretroviral therapy (ART) for HIV is vulnerable to unplanned treatment interruptions–consecutively missed doses over a series of days–which can result in virologic rebound. Yet clinicians lack a simple, valid method for estimating the risk of interruptions. If the likelihood of ART interruption could be derived from a convenient-to-gather summary measure of medication adherence, it might be a valuable tool for both clinical decision-making and research. We constructed an a priori probability model of ART interruption based on average adherence and tested its predictions using data collected on 185 HIV-infected, treatment-naïve individuals over the first 90 days of ART in a prospective cohort study in Mbarara, Uganda. The outcome of interest was the presence or absence of a treatment gap, defined as >72 hours without a dose. Using the pre-determined value of 0.50 probability as the cut point for predicting an interruption, the classification accuracy of the model was 73% (95% CI = 66%– 79%), the specificity was 87% (95% CI = 79%– 93%), and the sensitivity was 59% (95% CI = 48%– 69%). Overall model performance was satisfactory, with an area under the receiver operator characteristic curve (AUROC) of 0.85 (95% CI = 0.80–0.91) and Brier score of 0.20. The study serves as proof-of-concept that the probability model can accurately differentiate patients on the continuum of risk for short-term ART interruptions using a summary measure of adherence. The model may also aid in the design of targeted interventions.

It is important to identify those patients that will benefit from revascularization and those patients that will have an excellent prognosis on optimal medical treatment. Thus, there is a need of an easily performed diagnostic test in the cath lab that can accurately and reliably assess the physiological significance of a coronary artery stenosis. Fractional flow reserve (FFR) is a well-established method that has been proven to measure the functional significance of a stenosis. In the absence of a stenosis FFR is 1 and decreases depending on the significance of the stenosis. Instantaneous wave-free ratio (iFR) is a recently developed physiological index, which is obtained at rest without the need of hyperemic stimulation. Several trials have demonstrated that an iFR cut point of 0.89 matches an FFR cut point of 0.80.

An adaptive selecting cut point mechanism is designed to build a classifier.Adaptive removing attribute mechanism will remove the redundant attributes.We adopt two mechanisms to design algorithm which for classifier construction.Experimental results show the effectiveness and feasibility of our algorithm. Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. Minimal cost decision tree construction plays a crucial role in cost sensitive learning. Recently, many algorithms have been developed to tackle this problem. These algorithms choose an appropriate cut point of a numeric attribute by computing all possible cut points and assign a node through test all attributes. Therefore, the efficiency of these algorithms for large data sets is often unsatisfactory. To solve this issue, in this paper we propose a cost sensitive decision tree algorithm with two adaptive mechanisms to learn cost sensitive decision trees from training data sets based on C4.5 algorithm. The two adaptive mechanisms play an important role in cost sensitive decision tree construction. The first mechanism, adaptive selecting the cut point (ASCP) mechanism, selects the cut point adaptively to build a classifier rather than calculates each possible cut point of an attribute. It improves the efficiency of evaluating numeric attributes for cut point selection significantly. The second mechanism, adaptive removing attribute (ARA) mechanism, removes some redundant attributes in the process of selecting node. The effectiveness of the proposed algorithm is demonstrated on fourteen UCI data sets with representative test cost Normal distribution. Compared with the CS-C4.5 algorithm, the proposed algorithm significantly increases efficiency.

Most diagnostic accuracy measures and criteria for selecting optimal cut-points are only applicable to diseases with binary or three stages. Currently, there exist two diagnostic measures for diseases with general k stages: the hypervolume under the manifold and the generalized Youden index. While hypervolume under the manifold cannot be used for cut-points selection, generalized Youden index is only defined upon correct classification rates. This paper proposes a new measure named maximum absolute determinant for diseases with k stages ([Formula: see text]). This comprehensive new measure utilizes all the available classification information and serves as a cut-points selection criterion as well. Both the geometric and probabilistic interpretations for the new measure are examined. Power and simulation studies are carried out to investigate its performance as a measure of diagnostic accuracy as well as cut-points selection criterion. A real data set from Alzheimer’s Disease Neuroimaging Initiative is analyzed using the proposed maximum absolute determinant.

Biotherapeutics can elicit immune responses, which can alter the exposure, safety, and efficacy of the therapeutics. A well-designed and robust bioanalytical method is critical for the detection and characterization of relevant anti-drug antibody (ADA) and the success of an immunogenicity study. As a fundamental criterion in immunogenicity testing, assay cut points need to be statistically established with a risk-based approach to reduce subjectivity. This manuscript describes the development of a validated, web-based, multi-tier customized assay statistical tool (CAST) for assessing cut points of ADA assays. The tool provides an intuitive web interface that allows users to import experimental data generated from a standardized experimental design, select the assay factors, run the standardized analysis algorithms, and generate tables, figures, and listings (TFL). It allows bioanalytical scientists to perform complex statistical analysis at a click of the button to produce reliable assay parameters in support of immunogenicity studies.

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting., MINECO: MTM2010-14913, MTM2011-28285-C02-01 and MTM2013-40941-P. Basque Government: IT620-13, 2012111008. University of the Basque Country UPV/EHU: GIU10/21, UFI11/52. Agrupamento IN-BIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). Red IRYSS (Investigación en Resultados y Servicios Sanitarios)- of the Instituto de Salud Carlos III: G03/220

Background Personality disorder (PD) is associated with important health outcomes in the general population. However, the length of diagnostic interviews poses a significant barrier to obtaining large scale, population-based data on PD. A brief screen for the identification of people at high risk of PD in the general population could be extremely valuable for both clinicians and researchers. Aim We set out to validate the Standardised Assessment of Personality – Abbreviated Scale (SAPAS), in a general population sample, using the Structured Clinical Interviews for DSM-IV Personality Disorders (SCID-II) as a gold standard. Method One hundred and ten randomly selected, community-dwelling adults were administered the SAPAS screening interview. The SCID-II was subsequently administered by a clinical interviewer blind to the initial SAPAS score. Receiver operating characteristic analysis was used to assess the discriminatory performance of the SAPAS, relative to the SCID-II. Results Area under the curve for the SAPAS was 0.70 (95% CI = 0.60 to 0.80; p

A key issue in decision tree (DT) induction with continuous valued attributes is to design an effective strategy for splitting nodes. The traditional approach to solving this problem is adopting the candidate cut point (CCP) with the highest discriminative ability, which is evaluated by some frequency based heuristic measures. However, such methods ignore the class permutation of examples in the node, and they cannot distinguish the CCPs with the same or similar frequency information, thus may fail to induce a better and smaller tree. In this paper, a new concept, i.e., segment of examples, is proposed to differentiate the CCPs with same frequency information. Then, a new hybrid scheme that combines the two heuristic measures, i.e., frequency and segment, is developed for splitting DT nodes. The relationship between frequency and the expected number of segments, which is regarded as a random variable, is also given. Experimental comparisons demonstrate that the proposed scheme is not only effective to improve the generalization capability, but also valid to reduce the size of the tree.

An approach describing the spaces of iterations of resolvents with constant mappings is given. Its use allows one to construct (homogeneous, not algebraically homogeneous) cut-point spaces of arbitrary order.

Background and aims: Owing to the multiplicity of the key components of metabolic syndrome (MetS), its diagnosis is very complex. The lack of a unique definition is responsible for the prevalence variability observed among studies; therefore, a definition based on contin- uous variables was recommended. The aim of this study was to compare competing models of the MetS factor structure for selecting the one that explains the best clustering pattern and to propose an algorithm for computing MetS as a continuous variable. Methods and results: Data were from isolated Sardinian populations (n Z 8102). Confirmatory factor analysis (CFA) and two-group CFA by gender were performed to evaluate the sex- specific factor structure of MetS. After selecting the best model, an algorithm was obtained using factor loadings/residual variances. The quality of the MetS score was evaluated by the receiver operating characteristics curve and the area under the curve. Cross-validation was performed to validate the score and to determine the best cut point. The best fit model was a bifactor one with a general factor (MetS) and three specific factors (f1: obesity/adiposity trait; f2: hyper- tension/blood pressure trait; and f3: lipid trait). Gender-specific algorithms were implemented to obtain MetS scores showing a good diagnostic performance (0.80 specificity and 0.80 sensitivity for the cut point). Furthermore, cross-validation confirmed these results. Conclusion: These analyses suggested that the bifactor model was the most representative one. In addition, they provided a score and a cut point that are both clinically accessible and interpret- able measures for MetS diagnosis and likely useful for evaluating the association with adverse cardiovascular disease and diabetes and for investigating the MetS genetic component.

The classification scenario needs handling of more than one biomarker. The main objective of the work is to propose a multivariate receiver operating characteristic (MROC) model which linearly combines the markers to classify them into one of the two groups and also to determine an optimal cut point. Simulation studies are conducted for four sets of mean vectors and covariance matrices and a real dataset is also used to demonstrate the proposed model. Linear and quadratic discriminant analysis has also been applied to the above datasets in order to explain the ease of the proposed model. Bootstrapped estimates of the parameters of the ROC curve are also estimated.

Aims The aim of the study was to evaluate usefulness of capillary blood glucose (CBG) for diagnosis of gestational diabetes mellitus (GDM) in resource-constrained settings where venous plasma glucose (VPG) estimations may be impossible. Methods Consecutive pregnant women (n = 1031) attending antenatal clinics in southern India underwent 75-g oral glucose tolerance test (OGTT). Fasting, 1- and 2-h VPG (AU2700 Beckman, Fullerton, CA) and CBG (One Touch Ultra-II, LifeScan) were simultaneously measured. Sensitivity and specificity were estimated for different CBG cut points using the International Association of Diabetes in Pregnancy Study Groups (IADPSG) criteria for the diagnosis of GDM as gold standard. Bland–Altman plots were drawn to look at the agreement between CBG and VPG. Correlation and regression equation analysis were also derived for CBG values. Results Pearson’s correlation between VPG and CBG for fasting was r = 0.433 [intraclass correlation coefficient (ICC) = 0.596, p

Cut points in immunogenicity assays are used to classify future specimens into anti-drug antibody (ADA) positive or negative. To determine a cut point during pre-study validation, drug-naive specimens are often analyzed on multiple microtiter plates taking sources of future variability into account, such as runs, days, analysts, gender, drug-spiked and the biological variability of un-spiked specimens themselves. Five phenomena may complicate the statistical cut point estimation: i) drug-naive specimens may contain already ADA-positives or lead to signals that erroneously appear to be ADA-positive, ii) mean differences between plates may remain after normalization of observations by negative control means, iii) experimental designs may contain several factors in a crossed or hierarchical structure, iv) low sample sizes in such complex designs lead to low power for pre-tests on distribution, outliers and variance structure, and v) the choice between normal and log-normal distribution has a serious impact on the cut point. We discuss statistical approaches to account for these complex data: i) mixture models, which can be used to analyze sets of specimens containing an unknown, possibly larger proportion of ADA-positive specimens, ii) random effects models, followed by the estimation of prediction intervals, which provide cut points while accounting for several factors, and iii) diagnostic plots, which allow the post hoc assessment of model assumptions. All methods discussed are available in the corresponding R add-on package mixADA.

Many new treatments in cancer clinical trials tend to benefit a subset of patients more. To avoid unnecessary therapies and failure to recognize beneficial treatments, biomarker threshold models are often used to identify this subset of patients. We are interested in testing the treatment-biomarker interaction effects in a threshold model with biomarker but an unknown cut point. The unknown cut point causes irregularity in the model, and the traditional likelihood ratio test cannot be applied directly. A test for biomarker-treatment interaction effects is developed using a residual bootstrap method to approximate the distribution of the proposed test statistic. We evaluate the residual bootstrap and the permutation methods through extensive simulation study and find that the residual bootstrap method gives accurate test size, while the permutation method cannot control type $I$ error sometimes in the presence of main treatment effects. The proposed residual bootstrap test can be used to explore potential treatment-by-biomarker interaction in clinical studies. The findings can be applied to guide the follow-up trial design using biomarker as a stratification factor. We apply the proposed residual bootstrap method to data from Breast International Group (BIG) 1-98 randomized clinical trial and show that patients with high Ki-67 level may benefit more from Letrozole treatment.

Control based asthma management results in improved asthma outcomes. The Asthma Control Questionnaire (ACQ-6), is a widely used and well validated metric which strongly predicts future exacerbations. 1 It demarcates between controlled (C), partially controlled (P), and uncontrolled (U), based on cut point scores of 2 It discriminates GINA categories of C, P and U as Figure Legend Distribution of ACQ control categories relative to VAS levels. References Meltzeret al. JACI 2011;127:167–172. Ohtaet al. J Asthma2013;50:514–521.

Objective:To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis.Methods:Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66–90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004–2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC).Results:We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668–0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855–0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%.Conclusions:Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.

INTRODUCTION Reducing sedentary behavior has emerged as an important health intervention strategy. Although hip-worn, and more recently wrist-worn, accelerometers are commonly used for measuring physical activity and sedentary behavior, they may not provide accurate measures of postural changes. The current study examined the validity of commonly used hip- and wrist-worn accelerometer cut points and the thigh-worn activPAL activity monitor for measuring sit-to-stand transitions as compared with direct observation in youth with overweight and obesity. METHODS Nine children wore three activity monitors while being directly observed. The monitors included a hip- and wrist-worn ActiGraph and thigh-worn activPAL. The hip-worn ActiGraph was processed with the normal- and low-frequency filters and the inclinometer function. Cut points of ≤25 counts per 15-s epoch for the hip and ≤105 counts per 15-s epoch for the wrist were applied to the vertical axis to identify sit-to-stand transitions. Epoch-level absolute agreement, Bland-Altman plots, mixed-effects linear regression, and intraclass correlation coefficients (ICC) were investigated. RESULTS The hip and wrist accelerometer cut points and the hip inclinometer function overestimated the number of hourly sit-to-stand transitions by approximately triple as compared with direct observation. ICC values between the ActiGraph methods and the direct observation were all

Background Non-communicable diseases (NCDs) are an important issue worldwide. Obesity has a close relationship with NCDs. Various age-related changes should be considered when evaluating obesity. Methods National representative cohort data from the National Health Insurance Service National Sample Cohort from 2012 to 2013 were used. Sex-specific and age group-specific (10-year intervals) means for body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WtHR) were calculated. Optimal cut-points for obesity parameters were defined as the value predicting two or more components of metabolic syndrome (except WC). Results The mean value and optimal cut-point for BMI decreased with age for men. The mean BMI value for women increased with age, but optimal cut-points showed no remarkable difference. The mean WC of men increased with age, but the optimal cut-points were similar for age groups. For women, the mean value and optimal cut-point for WC increased with age. Regarding WtHR, the mean value and optimal cut-point increased with age for men and women. Differences across age groups were larger for women. Conclusion The mean values of the obesity indices and the optimal cut-points were changed according to age groups. This study supports the necessity of applying age group-specific cut-points for the various obesity parameters., Graphical Abstract

Crisp discretization is widely-used and easy to implement, but it cannot work well on some situations, for example, when there is no clear boundary between attribute values or the attribute values are close to a cut point and possibly affected by random noise. A discretization method named Softcut was proposed to accommodate such situations and has been applied to face recognition. Softcut introduced a parameter τ that defined a symmetrical interval (0.5-τ, 0.5+τ) named uncertain interval. The attribute values in (0.5-τ, 0.5+τ) were undiscretizable. However, because the performance of Softcut may deteriorate in some situations, an extension named eSoftcut was proposed, in which the uncertain interval was defined by two parameters, (τj1, τj2). In this article, the theoretical aspects of eSoftcut including its definition, algorithm, and properties are introduced.

Today, the assessment of immunogenicity is integral in nonclinical and clinical testing of new biotherapeutics and biosimilars. A key component in the risk-based evaluation of immunogenicity involves the detection and characterization of anti-drug antibodies (ADA). Over the past couple of decades, much progress has been made in standardizing the generalized approach for ADA testing with a three-tiered testing paradigm involving screening, confirmation, and quasi-quantitative titer assessment representing the typical harmonized scheme. Depending on a biotherapeutic’s structural attributes, more characterization and testing may be appropriate. Unlike bioanalytical assays used to support the evaluation of pharmacokinetics or toxicokinetics, an important component in immunogenicity testing is the calculation of cut points for the identification (screening), confirmation (specificity), and titer assessment responses in animals and humans. Several key publications have laid an excellent foundation for statistical design and data analysis to determine immunogenicity cut points. Yet, the process for statistical determination of cut points remains a topic of active discussion by investigators who conduct immunogenicity assessments to support biotherapeutic drug development. In recent years, we have refined our statistical approach to address the challenges that have arisen due to the evolution in biotherapeutics and the analytical technologies used for quasi-quantitative detection. Based on this collective experience, we offer a simplified statistical analysis process and flow-scheme for cut point evaluations that should work in a large majority of projects to provide reliable estimates for the screening, confirmatory, and titering cut points.

Purpose: To find spatial features that best predict acute duodenal related toxicity grade ≥ 3 from 2D dose surface maps in pancreatic chemoradiotherapy. Methods: Nineteen patients from the ARCII trial (EudraCT 2008-006302-42) treating locally-advanced pancreatic cancer (LAPC) with chemoradiotherapy were analysed. Previous work considering traditional dose-volume and dose-surface metrics as predictors had little power. We investigate the use of texture analysis to improve predictive capabilities. 2D duodenal dose surface maps (DSMs) were converted to 4, 8, and 16 gray (dose) levels. A gray-level co-occurrence matrix (GLCM) was produced and statistics (contrast, correlation, energy and homogeneity) were extracted. ROC curves were produced for each statistic for three different gray level maps (4, 8, and 16). The area under curve (AUC) was recorded and used to find the best predictor for acute duodenal related toxicity grade ≥ 3. Results: For all GLCM statistics there were only small differences in AUC between 4, 8 and 16 gray levels. Homogeneity had an AUC of 0.87, 0.88 and 0.85 for 4, 8 and 16 gray levels respectively. Using cut point analysis on homogeneity using 8 gray levels it was found that a value of 0.945 correctly predicted toxicity for 17/19 patients (4/6 patients with, 13/13 patients without toxicity). The two incorrectly predicted patients had a mean dose of 59 Gy and 28 Gy. The AUC for traditional dose-volume (V45 = 0.57, V50 = 0.60, V55 = 0.47) and dose-surface metrics (S45 (surface area receiving at least 45 Gy) = 0.50, S50 = 0.57, S55 = 0.49) were much lower. Conclusion: Use of texture analysis improves our ability to predict grade ≥ 3 GI toxicity in pancreatic chemoradiotherapy. The additional spatial information may give better predictors than traditional dose-volume and dose-surface metrics. The mechanism behind this predicted toxicity will be studied on a larger patient set. Alon Witztum is supported by an MRC/Gray Institute DPhil Studentship. Sam Warren and Mike Partridge are supported by CRUK grant C5255/A15935. Maria Hawkins received an MRC Fellowship MC_PC_12001/2.

PubMedID: 28642804 ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: Diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point. © 2017 Ilker Unal.

We characterize the subRiemannian cut locus of the origin in the free Carnot group of step two with three generators. We also calculate explicitly the cut time of any extremal path and the distance from the origin of all points of the cut locus. Finally, by using the Hamiltonian approach, we show that the cut time of strictly normal extremal paths is a smooth explicit function of the initial velocity covector. Finally, using our previous results, we show that at any cut point the distance has a corner-like singularity., Comment: Added Section 6. Final version, to appear on Calc. Var

Human activity can be measured through identification of bouts of activity. The Freedson cut point method used by ActiGraph has become one very common and well accepted standard for estimating times of continuous moderate to vigorous physical activity (MVPA). However, such methods do not directly apply to other data sources such as the Fitbit Flex, a wrist worn wireless pedometer. In previous research by the authors, a model was presented to improve the estimates of physical activity (PA) level in the Fitbit devices. This paper considers the estimates of activity bouts, building on the modeled PA level from the Fitbit Flex as compared to the results from the ActiGraph GT3X. The purpose of this paper is to compare the “gold standard” ActiGraph to modeled Fitbit Freedson methods and to establish normative values of expected errors in bout detection between the two devices and methods, both of which are proxy methods aimed at measuring actual physical activity levels. Here we compare bout identification using three measures, the ActiGraph Freedson method, Fitbit Intensity Score, and the modeled Fitbit Freedson using three different outcomes. First, we compare a baseline of per subject per day number and duration of bouts from an ActiGraph GT3X to the results found from using the same methods on the Intensity Score reported by Fitbit and the modeled Fitbit Freedson method. Next, we compare the difference in duration of bouts identified in each data source matched according to similar start and end times. Finally, we compare the bouts found from the three methods to bouts identified in a self report diary.

Response to anemia in low-resource settings may entail presumptive iron treatment for those with Hemoglobin (Hb) levels falling below certain cut points. This study aimed to inform an anemia screening and treatment service in a low-income community in the Dominican Republic by determining (i) the prevalence of anemia in young children attending this service using different Hb cut points and (ii) the extent of microcytosis using different recommended cut points for the mean corpuscular volume (MCV). Using the WHO recommended cut point of

In biomedical research and practice, continuous biomarkers are often used for diagnosis and prognosis, with a cut-point being established on the measurement to aid binary classification. When survival time is examined for the purposes of disease prognostication and is found to be related to the baseline measure of a biomarker, employing a single cut-point on the biomarker may not be very informative. Using survival time-dependent sensitivity and specificity, we extend a concordance probability-based objective function to select survival time-related cut-points. To estimate the objective function with censored survival data, we adopt a non-parametric procedure for time-dependent receiver operational characteristics curves, which uses nearest neighbor estimation techniques. In a simulation study, the proposed method, when used to select a cut-point to optimally predict survival at a given time within a specified range, yields satisfactory results. We apply the procedure to estimate survival time-dependent cut-point on the prognostic biomarker of serum bilirubin among patients with primary biliary cirrhosis.

Administration of biological therapeutics can generate undesirable immune responses that may induce anti-drug antibodies (ADAs). Immunogenicity can negatively affect patients, ranging from mild reactive effect to hypersensitivity reactions or even serious autoimmune diseases. Assessment of immunogenicity is critical as the ADAs can adversely impact the efficacy and safety of the drug products. Well-developed and validated immunogenicity assays are required by the regulatory agencies as tools for immunogenicity assessment. Key to the development and validation of an immunogenicity assay is the determination of a cut point, which serves as the threshold for classifying patients as ADA positive(reactive) or negative. In practice, the cut point is determined as either the quantile of a parametric or nonparametric empirical distribution. The parametric method, which is often based on a normality assumption, may lead to biased cut point estimates when the normality assumption is violated. The non-parametric method, which yields unbiased estimates of the cut point, may have low efficiency when the sample size is small. As the distribution of immune responses are often skewed and sometimes heavy-tailed, we propose two non-normal random effects models for cut point determination. The random effects, following a skew-t or log-gamma distribution, can incorporate the skewed and heavy-tailed responses and the correlation among repeated measurements. Simulation study is conducted to compare the proposed method with the current normal and nonparametric alternatives. The proposed models are also applied to a real dataset generated from assay validation studies.

This paper studies the asymptotic behaviour of the false discovery and non-discovery proportions of the dynamic adaptive procedure under some dependence structure. A Bahadur-type representation of the cut point in simultaneously performing a large scale of tests is presented. The asymptotic bias decompositions of the false discovery and non-discovery proportions are given under some dependence structure. In addition to existing literatures, we find that the randomness due to the dynamic selection of the tuning parameter in estimating the true null rate serves as a source of the approximation error in the Bahadur representation and enters into the asymptotic bias term of the false discovery proportion and those of the false non-discovery proportion. The theory explains to some extent why some seemingly attractive dynamic adaptive procedures do not outperform the competing fixed adaptive procedures substantially in some situations. Simulations justify our theory and findings.

Objective The aim of this study was to evaluate the validity of the seven-item Generalized Anxiety Disorder scale (GAD-7) and its two core items (GAD-2) for detecting GAD in elderly people. Methods A criterion-standard study was performed between May and December of 2010 on a general elderly population living at home. A subsample of 438 elderly persons (ages 58–82) of the large population-based German ESTHER study was included in the study. The GAD-7 was administered to participants as part of a home visit. A telephone-administered structured clinical interview was subsequently conducted by a blinded interviewer. The structured clinical (SCID) interview diagnosis of GAD constituted the criterion standard to determine sensitivity and specificity of the GAD-7 and the GAD-2 scales. Results Twenty-seven participants met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for current GAD according to the SCID interview (6.2%; 95% confidence interval [CI]: 3.9%–8.2%). For the GAD-7, a cut point of five or greater appeared to be optimal for detecting GAD. At this cut point the sensitivity of the GAD-7 was 0.63 and the specificity was 0.9. Correspondingly, the optimal cut point for the GAD-2 was two or greater with a sensitivity of 0.67 and a specificity of 0.90. The areas under the curve were 0.88 (95% CI: 0.83–0.93) for the GAD-7 and 0.87 (95% CI: 0.80–0.94) for the GAD-2. The increased scores on both GAD scales were strongly associated with mental health related quality of life (p Conclusion Our results establish the validity of both the GAD-7 and the GAD-2 in elderly persons. Results of this study show that the recommended cut points of the GAD-7 and the GAD-2 for detecting GAD should be lowered for the elderly general population.