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Abstract Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global–Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback–Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as “missed.” The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity

The aim of this study is to evaluate the compatibility of the "Computed Tomography (CT)" and "Magnetic Resonance (MR)" methods which are imaging techniques used to describe prostate cancer with the pathology accepted as the reference method. In this study, the concordance between CT and MR results and pathology results of 37 prostate cancer patients was evaluated using the Bland-Altman, Interclass Correlation Coefficient, Concordance Correlation Coefficient, Deming Regression and Passing-Bablok method comparison methods. Inter-class correlation coefficient and Concordance Correlation Coefficient values for CT-Pathology results were 0.62 and 0.62, respectively. Inter-class correlation coefficient and Concordance Correlation Coefficient values for MR-Pathology results were 0.74 and 0.75, respectively. The regression equation for the Deming regression method is y=-6.21+1.03x for CT-Pathology, whereas for the MR-Pathology y=-6.86+1.11x. When the CT-Pathology measurement values are evaluated by the Bland-Altman statistical method, the mean values of the measurement differences are -2.42 and the standard deviation is 33.50. When the agreement between MR-Pathology measurement values is examined by the Bland-Altman method, the mean values for the difference between the measurement values are 4.14 and the standard deviation is 26.78. Among the applied methods, the Bland-Altman method is the most suitable method for data structure. According to the results of the Bland-Altman method, the tumor size obtained by the pathology method can be found to be 2.4 smaller than the mean values obtained from the CT results and an average of 4.1 cc from the measurement values obtained by the MR imaging technique. [ABSTRACT FROM AUTHOR]

Modern medical devices are increasingly producing complex data that could offer deeper insights into physiological mechanisms of underlying diseases. One type of complex data that arises frequently in medical imaging studies is functional data, whose sampling unit is a smooth continuous function. In this work, with the goal of establishing the scientific validity of experiments involving modern medical imaging devices, we focus on the problem of evaluating reliability and reproducibility of multiple functional data that are measured on the same subjects by different methods (i.e. different technologies or raters). Specifically, we develop a series of intraclass correlation coefficient and concordance correlation coefficient indices that can assess intra-method, inter-method, and total (intra + inter) agreement based on multivariate multilevel functional data consisting of replicated functional data measurements produced by each of the different methods. For efficient estimation, the proposed indices are expressed using variance components of a multivariate multilevel functional mixed effect model, which can be smoothly estimated by functional principal component analysis. Extensive simulation studies are performed to assess the finite-sample properties of the estimators. The proposed method is applied to evaluate the reliability and reproducibility of renogram curves produced by a high-tech radionuclide image scan used to non-invasively detect kidney obstruction. [ABSTRACT FROM AUTHOR]

Background: Half-marathon races have become increasingly more popular with many recreational athletes all around the world. New and recreational runners are likely to have the greatest need for training advice to set running paces during long-distance races. Purpose: To develop a simple equation to estimate half-marathon time from the Cooper test and verify its validity. Methods: One hundred ninety-eight recreational runners (177 men and 21 women, 40 [6.8] years and 33.7 [8] years, respectively) participated in this study. All runners completed the Cooper test 7 to 10 days prior to races. A stepwise multiple regression analysis was performed to select the main predictors of half-marathon time. Results: Simple correlation analysis showed that Cooper test performance (distance) was a good construct to estimate half-marathon time (r = −.906; 95% confidence interval, −0.927 to −0.877; P <.0001). The authors also derived an equation with a high predictive validity (R2 =.82; standard error of estimation = 5.19 min) and low systematic bias (mean differences between the predicted value and the criterion of 0.48 [5.2] min). Finally, the concordance coefficient of correlation (.9038) and proportional bias analysis (Kendall τ = −.0799; 95% confidence interval, −0.184 to 0.00453; P =.09) confirmed a good concurrent validity. Conclusion: In this study, the authors derived an equation from the Cooper test data with a high predictive and concurrent validity and low bias. [ABSTRACT FROM AUTHOR]

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The concordance correlation coefficient (CCC) is a common tool to assess agreement among multiple observers for continuous and discrete responses. However, previous results in the statistical literature have shown that the CCC estimators may suffer from a bias problem under a misspecified model for normal data. In order to avoid fitting data with a misspecified model, thus yielding biased CCC estimates for longitudinal overdispersed Poisson data, this research proposes new model-averaged estimators of CCC by combining the estimators of the variance components (VC) approach with model selection via corrected conditional Akaike information criterion (CAICC) and corrected conditional Bayesian information criterion (CBICC) measures under extended overdispersed three-way Poisson mixed-effects models. In simulation studies, the performance of the proposed model-averaged estimators is compared with the VC estimators with and without model selection via CAICC and CBICC and other existing model-averaged estimators for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is presented for illustration. It can be concluded that the proposed model-averaged approach is a reliable procedure yielding small mean square errors and nominal 95% coverage rates. Therefore, the new model-averaged estimator is more robust to model misspecification than other competitors. [ABSTRACT FROM AUTHOR]

Abstract Background The Bland-Altman plot with the limits of agreement has been widely used as an absolute index for assessing test-retest reliability or reproducibility between two measurements. We have observed that in the settings where the relative index such as concordance correlation coefficient (CCC) or intraclass correlation coefficient is employed, the limits of agreement approach may be inconsistent with the scaled index. Particularly, the broad width of the limits of agreement may indicate a lack of agreement when the two measurements are highly concordant but an acceptable difference is not known and the common variance of the data is large. This research aims to create a novel, CCC-based guidance for graphical evaluation of reproducibility or reliability. Methods The concordance correlation coefficient is used to create a 100(1-α)% reference band from two measurements. Simulation studies and real examples, including the peak expiratory flow rate data in Bland and Altman’s paper and the test-retest reproducibility data of the Radiomics study, are implemented to assess the use of the reference band. Results In the absence of an acceptable difference between measurements, we found that the limits of agreement may not be consistent with the concordance correlation coefficient. Our simulation study results and real data application show that the proposed method can provide practitioners with a novel graphical evaluation that is consistent with results from the concordance correlation coefficient. Conclusions Our proposed novel scaled index-based guidance can be used for the graphical evaluation of reproducibility or reliability and may have advantages over the limits of agreement in settings where the concordance correlation coefficient is employed.

Purpose: The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). Approach: Two cohorts of non‐small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV < 10% and CCC > 0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. Results: In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. Conclusions: Radiomic feature uniqueness should be considered to improve the reliability of radiomics study. [ABSTRACT FROM AUTHOR]

This note examines, at the population-level, the approach of obtaining predictors h ˜ (X) of a random variable Y, given the joint distribution of (Y , X) , by maximizing the mapping h ↦ κ (Y , h (X)) for a given correlation function κ (· , ·) . Commencing with Pearson's correlation function, the class of such predictors is uncountably infinite. The least-squares predictor h * is an element of this class obtained by equating the expectations of Y and h (X) to be equal and the variances of h (X) and E (Y | X) to be also equal. On the other hand, replacing the second condition by the equality of the variances of Y and h (X) , a natural requirement for some calibration problems, the unique predictor h * * that is obtained has the maximum value of Lin's (1989) concordance correlation coefficient (CCC) with Y among all predictors. Since the CCC measures the degree of agreement, the new predictor h * * is called the maximal agreement predictor. These predictors are illustrated for three special distributions: the multivariate normal distribution; the exponential distribution, conditional on covariates; and the Dirichlet distribution. The exponential distribution is relevant in survival analysis or in reliability settings, while the Dirichlet distribution is relevant for compositional data. [ABSTRACT FROM AUTHOR]

The understanding of white matter (WM) was revolutionized by the emergence of tractography based on diffusion tensor imaging (DTI). Currently, DTI simulations are implemented in preoperative planning to optimize surgical approaches. The reliability of these simulations has been questioned and investigated seeking for correlation between neurological performance and anomalies in DTI parameters. However, the ability of preoperative WM simulations to predict a surgical injury has not been thoroughly evaluated. Our objective was to assess the reliability of preoperatively simulated WM injuries for conventional neurosurgical procedures. WM surgical damage was preoperatively simulated by creating a 3-dimensional volume representing the endoscope or the surgical trajectory. This volume was used as an additional region of interest in the fascicle reconstruction to be subtracted from the original fascicle. Simulated, injured fascicles were compared in terms of the number of fibers and volume to those created from postoperative DTI studies. Reliability was assimilated into the correlation between the simulation and the postoperative reconstruction; evaluated using the intraclass correlation coefficient or Lin's Concordance correlation coefficient (CCC), and represented on Bland–Altman plots. The preoperative and postoperative DTI studies of 30 patients undergoing various neurosurgical approaches were processed. The correlation between simulated injuries and postoperative studies was high in terms of fibers (Concordance correlation coefficient = Rho.C = 0.989 [95% confidence interval = 0.979–0.995]) and volume (intraclass correlation coefficient = 0.95 [95% CI = 0.89–0.97]). Bland–Altman plots demonstrated that the great majority of cases fell within the mean ± 2 Standard deviations. Presurgical simulation of WM fascicles based on DTI is consistent with postoperative DTI studies. These findings require further validation by neurophysiological and clinical correlation. [ABSTRACT FROM AUTHOR]

Previous research points out high relevance of maximal strength measurement in the diagnostics of different populations. However, there is inconsistency in procedures of maximum strength measurements. Thus, it must be questioned whether the results from different studies are actually comparable. Due to the aforementioned problems in standardization, the aim of this study was to assess correlations between and reproducibility of isometric and dynamic strength testing. Since there are many studies investigating maximal strength in the calf muscles, this study will examine the plantar flexors. For this purpose, 87 active participants were recruited (m: 52, f: 35, age: 28.3 ± 5.5 years, range 18-38 years, height: 178.3 ± 6.6 cm, weight: 81.5 ± 7.4 kg) who performed maximal isometric strength testing and dynamic 1RM testing in plantar flexion both with extended and bended knee joint. Pearson correlation as well as concordance correlation coefficient (CCC) were determined. In literature, CCC is used to determine reproducibility between two different testing methods. There were correlation coefficients of r = 0.63 - 0.77 and ρC=0.62 - 0.77. Results are comparable with correlations between maximal isometric strength and 1-RM in previous studies in different movements. In consideration of CCC, isometric strength testing and 1RM seem to not measure the same parameter, therefore comparison of results measured with different procedures seems difficult. Our results exhibit a high influence of isometric maximal strength on 1RM performance. However, 1RM tests cannot be replaced by isometric strength testing. Care must be taken due to standardization of procedure when comparing results from different studies and, especially, if 1RM testing should be replaced with isometric strength measurement. [ABSTRACT FROM AUTHOR]

Abstract Background Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects. Methods We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement. Results The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others. Conclusions Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this.

Background: The Bland-Altman plot with the limits of agreement has been widely used as an absolute index for assessing test-retest reliability or reproducibility between two measurements. We have observed that in the settings where the relative index such as concordance correlation coefficient (CCC) or intraclass correlation coefficient is employed, the limits of agreement approach may be inconsistent with the scaled index. Particularly, the broad width of the limits of agreement may indicate a lack of agreement when the two measurements are highly concordant but an acceptable difference is not known and the common variance of the data is large. This research aims to create a novel, CCC-based guidance for graphical evaluation of reproducibility or reliability.Methods: The concordance correlation coefficient is used to create a 100(1-α)% reference band from two measurements. Simulation studies and real examples, including the peak expiratory flow rate data in Bland and Altman's paper and the test-retest reproducibility data of the Radiomics study, are implemented to assess the use of the reference band.Results: In the absence of an acceptable difference between measurements, we found that the limits of agreement may not be consistent with the concordance correlation coefficient. Our simulation study results and real data application show that the proposed method can provide practitioners with a novel graphical evaluation that is consistent with results from the concordance correlation coefficient.Conclusions: Our proposed novel scaled index-based guidance can be used for the graphical evaluation of reproducibility or reliability and may have advantages over the limits of agreement in settings where the concordance correlation coefficient is employed. [ABSTRACT FROM AUTHOR]

For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation coefficient for longitudinal overdispersed Poisson data. Further, the indices of intra-, inter-, and total agreement through generalized estimating equations are also developed considering the correlation structure of longitudinal count repeated measurements. Simulation studies are conducted to compare the performance of the modified variance component and generalized estimating equation approaches for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is used for illustration. In conclusion, the modified variance component approach performs outstandingly well with small mean square errors and nominal 95% coverage rates. The generalized estimating equation approach provides in model assumption flexibility of correlation structures for repeated measurements to produce satisfactory concordance correlation coefficient estimation results. [ABSTRACT FROM AUTHOR]

Context. Predictive equations estimate total fat mass obtained from multiple-site ultrasound measurements; however, the predictive equation of total fat mass has not been investigated solely from abdominal subcutaneous fat thickness. Objective. To develop regression-based prediction equations using abdominal subcutaneous fat thickness for predicting fat mass, and to explore the validity of these predictive equations. Design. Cross-sectional study. Subjects and Methods. Twenty-seven males and eighteen females were randomly divided into two groups as the model prediction and the validation. Total body fat mass was determined by dual-energy X-ray absorptiometry. The linear regression analysis was used to predict equations for total body fat mass from abdominal subcutaneous fat thickness acquired by ultrasound. Then, these predictive equations were tested on the validation group. Lin's concordance correlation coefficient (CCC) was used as a further measure of agreement. Results. When fat mass prediction equations were tested on the validation groups, measured- and estimated-total fat masses in males (p=0.9) and females (p=0.5) were found similar. A good level of agreement between measurements in males (CCC=0.84) and females (CCC=0.76) was attained. Conclusion. The abdominal subcutaneous fat thickness obtained from a single region by ultrasound might provide a non-invasive and quick evaluation. [ABSTRACT FROM AUTHOR]

Systematic reviews with meta‐analyses are powerful tools that can answer research questions based on data from published studies. Ideally, all relevant data is directly available in the text or tables, but often it is only presented in graphs. In those cases, the data can be extracted from graphs, but this potentially introduces errors. Here, we investigate to what extent the extracted outcome and error values differ from the original data and if these differences could affect the results of a meta‐analysis. Six extractors extracted 36 outcome values and corresponding errors from 22 articles. Differences between extractors were compared using overall concordance correlation coefficients (OCCC), differences between the original and extracted data were compared using concordance correlation coefficients (CCC). To test the possible influence on meta‐analyses, random‐effects meta‐analyses on mean difference comparing original and extracted data were performed. The OCCCs and CCCs were high for both outcome values and errors, CCCs were >0.99 for the outcome and >0.92 for errors. The meta‐analyses showed that the overall effect on outcome was very small (median: 0.025, interquartile range: 0.016–0.046). Therefore, data extraction from graphs is a good method to harvest data if it is not provided in the text or tables, and the original authors cannot provide the data. [ABSTRACT FROM AUTHOR]

Introduction: Automated blood pressure (BP) monitor is widely used to assess the blood pressure (BP) of the study subjects in community-based researches. This study aims at the detection of hypertension by automated BP device and examines the concordance and validity between automated and mercury-free LED BP devices. Materials and Methods: This cross-sectional study was conducted in the tribal state of Jharkhand in India from January 2017 to June 2017. A total of 300 study participants aged more than 18 years were enrolled in this study. BP of the patients in the sitting position was measured three times each by automated device and mercury-free LED BP device. The different sets of readings were assessed by concordance correlation coefficient (CCC) and other validity measures. Results: The CCC for systolic and diastolic BP measured by automated and mercury-free LED BP is 0.88 and 0.85, respectively. The mean difference between systolic and diastolic BP by both the instruments is statistically insignificant (P > 0.05). The sensitivity, specificity, PPV, NPV, and accuracy of automated BP devices to predict hypertension is 96.61%, 92.21%, 75%, 99%, and 93%. The area under ROC for systolic and diastolic BP is 0.984 and 0.97, respectively with P values < 0.0001 in both the cases. Conclusions: This study concluded that the overall automated BP machine has fair degree of agreement (CCC) with a manual BP device. The validity of this monitor to screen hypertension may also be considered in field settings.

Background: Body mass index (BMI) is often criticised since it doesn’t consider sex, age and ethnicity, which may affect the height scaling exponent of the equation. Aims: First, to identify specific height scaling exponents (α) based on sex, age and ethnicity. Second, to assess the performance of the current vs the proposed BMI equations (1) to predict total fat mass (TFM) and metabolic syndrome (MetS) severity and (2) to correctly identify obese individuals and those having MetS. Methods: In total, 41,403 individuals aged 20–80 years (NHANES, 1999–2014) were studied. Specific “α” were identified using the Benn formula. Various statistical approaches were performed to assess performances of the current vs the proposed-BMIs. Results: The proposed “α” varies from 1.2 to 2.5, after considering sex, age and ethnicity. BMIs calculated using the proposed “α” showed a similar capacity to predict TFM and MetS severity and to correctly identify obese individuals and those having MetS compared to the current BMI. Conclusions: Despite sex, age and ethnicity modulating the height scaling exponent of the BMI equation, using these proposed exponents in the BMI equation didn’t improve the capacity to predict TFM and MetS severity, suggesting that the current BMI remains a valid clinical tool.

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms. [ABSTRACT FROM AUTHOR]

Evaluating the reproducibility or agreement of microbiome measurements is often a crucial step to ensure rigorous downstream analyses in microbiome studies. In this paper, we address this need by developing adaptations of Lin's concordance correlation coefficient (CCC) tailored to microbiome studies. We introduce a general formulation of the new CCC measures upon the use of a distance function appropriately characterizing the discrepancy between microbiome compositional measurements. We thoroughly study the special cases that adopt the Euclidean distance and Aitchison distance. Our proposals appropriately account for the unique features of microbiome compositional data, including high‐dimensionality, dependency among individual relative abundances and the presence of many zeros. We further investigate a practical compound approach to help better understand the sources of data inconsistency. Extensive simulation studies are conducted to evaluate the utility of the proposed methods in realistic scenarios. We also apply the proposed methods to a microbiome validation data set from the Feeding Infants Right.. from the STart (FIRST) study. Our analyses offer useful insight about the extent of data variations resulted from two different experiment procedures as well as their heterogeneous patterns across genera. [ABSTRACT FROM AUTHOR]

Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters. Large sample‐based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample‐based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines. [ABSTRACT FROM AUTHOR]

Background: Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects.Methods: We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement.Results: The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others.Conclusions: Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this. [ABSTRACT FROM AUTHOR]

A non-intrusive sleep apnea detection system using a C-Band channel sensing technique is proposed to monitor sleep apnea syndrome in real time. The system utilizes perturbations of RF signals to differentiate between patient's breathing under normal and sleep apnea conditions. The peak distance calculation is used to obtain the respiratory rates. A comparison of the datasets generated by the proposed method and a wearable sensor is made using a concordance correlation coefficient to establish its accuracy. The results show that the proposed sensing technique exhibits high accuracy and robustness, with more than 80% concordance with the wearable breathing sensor. This method is, therefore, a good candidate for the real-time wireless detection of sleep apnea.

Introduction: Automated blood pressure (BP) monitor is widely used to assess the blood pressure (BP) of the study subjects in community-based researches. This study aims at the detection of hypertension by automated BP device and examines the concordance and validity between automated and mercury-free LED BP devices. Materials and Methods: This cross-sectional study was conducted in the tribal state of Jharkhand in India from January 2017 to June 2017. A total of 300 study participants aged more than 18 years were enrolled in this study. BP of the patients in the sitting position was measured three times each by automated device and mercury-free LED BP device. The different sets of readings were assessed by concordance correlation coefficient (CCC) and other validity measures. Results: The CCC for systolic and diastolic BP measured by automated and mercury-free LED BP is 0.88 and 0.85, respectively. The mean difference between systolic and diastolic BP by both the instruments is statistically insignificant (P > 0.05). The sensitivity, specificity, PPV, NPV, and accuracy of automated BP devices to predict hypertension is 96.61%, 92.21%, 75%, 99%, and 93%. The area under ROC for systolic and diastolic BP is 0.984 and 0.97, respectively with P values < 0.0001 in both the cases. Conclusions: This study concluded that the overall automated BP machine has fair degree of agreement (CCC) with a manual BP device. The validity of this monitor to screen hypertension may also be considered in field settings. [ABSTRACT FROM AUTHOR]

When a method comparison study is performed, the aim is to evaluate the agreement of measurements of different methods. We present the Bland–Altman plot with limits of agreement as the correct analysis methodology. We also discuss other scaled and unscaled indices of agreement and commonly used inappropriate approaches. [ABSTRACT FROM AUTHOR]

Background: Retrospective timeline follow-back (TLFB) data and prospective daily process data have been frequently collected in addiction research to characterize behavioral patterns. Although previous validity studies have demonstrated high correlations between these two types of data, the conventional method adopted in those studies was based on summary measures that may lose critical information and the Pearson's correlation coefficient that has an undesirable property. This study proposes the functional concordance correlation coefficient to address these issues.Methods: We use real data collected from a randomized experiment to demonstrate the applications of the proposed method and compare its analytical results with those of the conventional method. We also conduct a simulation study based on the real data to evaluate the level of overestimation associated with the conventional method.Results: The results of the real data example indicate that the correlation between these two types of data varies across substances (alcohol vs. marijuana) and assessment schedules (daily vs. weekly). Additionally, the correlations estimated by the conventional method tend to be higher than those estimated by the proposed method. The simulation results further show that the magnitude of overestimation associated with the conventional method is greatest when the true correlation is medium.Conclusions: The findings of the real data example imply that daily assessments are particularly beneficial for characterizing more variable behaviors like alcohol use, whereas weekly assessments may be sufficient for low variation events such as marijuana use. The proposed method is a better approach for evaluating the validity of TLFB data. [ABSTRACT FROM AUTHOR]

The concordance correlation coefficient (CCC) and the probability of agreement (PA) are two frequently used measures for evaluating the degree of agreement between measurements generated by two different methods. In this paper, we consider the CCC and the PA using the bivariate normal distribution for modeling the observations obtained by two measurement methods. The main aim of this paper is to develop diagnostic tools for the detection of those observations that are influential on the maximum likelihood estimators of the CCC and the PA using the local influence methodology but not based on the likelihood displacement. Thus, we derive first‐ and second‐order measures considering the case‐weight perturbation scheme. The proposed methodology is illustrated through a Monte Carlo simulation study and using a dataset from a clinical study on transient sleep disorder. Empirical results suggest that under certain circumstances first‐order local influence measures may be more powerful than second‐order measures for the detection of influential observations. [ABSTRACT FROM AUTHOR]

Background: Body mass index (BMI) is often criticised since it doesn't consider sex, age and ethnicity, which may affect the height scaling exponent of the equation. Aims: First, to identify specific height scaling exponents (α) based on sex, age and ethnicity. Second, to assess the performance of the current vs the proposed BMI equations (1) to predict total fat mass (TFM) and metabolic syndrome (MetS) severity and (2) to correctly identify obese individuals and those having MetS. Methods: In total, 41,403 individuals aged 20–80 years (NHANES, 1999–2014) were studied. Specific "α" were identified using the Benn formula. Various statistical approaches were performed to assess performances of the current vs the proposed-BMIs. Results: The proposed "α" varies from 1.2 to 2.5, after considering sex, age and ethnicity. BMIs calculated using the proposed "α" showed a similar capacity to predict TFM and MetS severity and to correctly identify obese individuals and those having MetS compared to the current BMI. Conclusions: Despite sex, age and ethnicity modulating the height scaling exponent of the BMI equation, using these proposed exponents in the BMI equation didn't improve the capacity to predict TFM and MetS severity, suggesting that the current BMI remains a valid clinical tool. [ABSTRACT FROM AUTHOR]

The need for assessment of agreement of biomarkers is ubiquitous in drug development. In this study, we focus on scaled agreement indices including within-subject coefficient of variation, intraclass correlation coefficient, and concordance correlation coefficient. We illustrate, by both simulated and real life datasets, the usage and added value of Bayesian estimation of agreement of biomarkers in early drug development. We discuss the solutions to small sample size, outliers, and nonnormally distributed data problems. Furthermore, we present how to address, coherently within the Bayesian framework, other practically relevant issues such as accommodation of covariates and model diagnostics. [ABSTRACT FROM AUTHOR]

The values of parameters calculated from distribution and abundance of the selected pentacyclic terpanes in crude oils from Libya and Serbia, which were originally derived from gas chromatography-mass spectrometry (GC–MS) were compared with results of quantification based on gas chromatography– –mass spectrometry-mass spectrometry (GC–MS-MS). The parameters analyzed are the most often used terpane source and maturity parameters, which were applied to a large sample set of 70 oils, originating from five oil fields. The aim of the paper was to investigate to which extent the measurements of the selected parameters by these two instrumental techniques agree and to determine the influence of differences between parameter values on geochemical interpretation. For that purpose two methods, concordance correlation coefficient and mean-difference plot were used. The obtained results indicate that calculation of C2718α(H)-22,29,30-trisnorneohopane/(C2718α(H)-22,29,30- -trisnorneohopane + C2717α(H)-22,29,30-trisnorhopane), C2918α(H)-30-norneohopane/ C2917α(H)21β(H)-30-norhopane and C2917α(H)21β(H)-30-norhopane/ C3017α(H)21β(H)-hopane ratios either by GC–MS or GC–MS-MS do not significantly influence interpretation. On the other hand, the determination of C3017β(H)21α(H)-moretane/C3017α(H)21β(H)-hopane ratio, gammacerane index and oleanane index by GC–MS vs. GC-MS-MS could notably affect interpretation. These differences can be explained by the co-elution and the peak overlapping in GC–MS but also by better separation, higher precision and better selectivity of the GC–MS-MS. Deviation of the almost all studied parameters from the line of equality was similar for the oils from the same oil field but some differences were observed for the oils from different oil fields. Therefore, when GC–MS-MS results are to be used in organic geochemical interpretations, a regional calibration of GC–MS vs. GC-MS-MS relationship for each petroleum system is highly recommended. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 176006]

In many clinical studies, Lin's (1989) concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. Most commonly, it is used under the assumption that data are normally distributed. However, in many practical applications, data are often skewed and/or thick-tailed. King and Chinchilli (2001) proposed robust estimation methods of alternative CCC indices, and we propose an approach that extends the existing methods of robust estimators by focusing on functionals that yield robust L-statistics. We provide two data examples to illustrate the methodology, and we discuss the results of computer simulation studies that evaluate statistical performance. [ABSTRACT FROM AUTHOR]

We compared the accuracy of visual assessment of landscape composition to that of Geographic Information Systems (GIS) mapping in (agro)ecology studies that focus on processes in which landscape factors are assumed to be insignificant and are often ignored. Seven land-use types were visually estimated from aerial photographs of contrasting rural landscapes in France. Visual assessment showed good agreement with results of GIS mapping for all land-use types, regardless of the observer. Estimates were accurate for forests and water areas, which are more visible than other land-use types. In contrast, observers often failed to distinguish crops from grassland, which decreased agreement for these land-use types; however, the estimate became fairly accurate when these land-use types were combined. Overall, this study confirms the relevance of using visual assessment to obtain simple landscape indices to improve studies that might otherwise ignore the landscape, provided that the loss of precision is compatible with the purpose of the study. [ABSTRACT FROM AUTHOR]

The Test of Practical Judgment (TOP-J) and the Judgment subtest from the Neuropsychological Assessment Battery (NAB-JDG) are both brief interview-based measures that assess judgment. This study compared estimates of judgment obtained from these measures in a neurodegenerative disease population. Records from 61 referrals seen for neuropsychological evaluation in a neurodegenerative disorders clinic were reviewed. Measures of interest included the TOP-J and NAB-JDG. Concordance correlation coefficients (CCC) and root mean square differences (RMSD) were calculated between judgment T-scores. Discrepancy scores were calculated by subtracting NAB-JDG scores from TOP-J scores. CCC showed poor agreement between the judgment measures, with evidence of fixed bias, such that the NAB-JDG systematically generates higher scores than the TOP-J. This fixed bias was present whether NAB-JDG scores are demographically adjusted or unadjusted. There was no evidence of proportional bias. In a neurodegenerative disease clinic population, the TOP-J and NAB-JDG provide estimates of judgment ability that are systematically different. These two measures may be assessing different aspects within the larger construct of judgment and the inconsistency between measures would contraindicate using them interchangeably. Clinicians will need to carefully consider patient characteristics, clinical needs, and review specific item content when selecting between these measures. [ABSTRACT FROM AUTHOR]

The concordance correlation coefficient (CCC) is a widely used scaled index in the study of agreement. In this article, we propose estimating the CCC by a unified Bayesian framework that can (1) accommodate symmetric or asymmetric and light- or heavy-tailed data; (2) select model from several candidates; and (3) address other issues frequently encountered in practice such as confounding covariates and missing data. The performance of the proposal was studied and demonstrated using simulated as well as real-life biomarker data from a clinical study of an insomnia drug. The implementation of the proposal is accessible through a package in the Comprehensive R Archive Network. [ABSTRACT FROM AUTHOR]