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Measures of agreement are used in a wide range of behavioral, biomedical, psychosocial, and health-care related research to assess reliability of diagnostic test, psychometric properties of instrument, fidelity of psychosocial intervention, and accuracy of proxy outcome. The concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. In modern-day applications, data are often clustered, making inference difficult to perform using existing methods. In addition, as longitudinal study designs become increasingly popular, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data and other related issues for performing CCC analysis within a longitudinal data setting. The approach is illustrated with both real and simulated data.

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

Background: The association between human blood DNA global methylation and global hydroxymethylation has not been evaluated in population-based studies. No studies have evaluated environmental determinants of global DNA hydroxymethylation, including exposure to metals. Objective: We evaluated the association between global DNA methylation and global DNA hydroxymethylation in 48 Strong Heart Study participants for which selected metals had been measured in urine at baseline and DNA was available from 1989–1991 (visit 1) and 1998–1999 (visit 3). Methods: We measured the percentage of 5-methylcytosine (5‑mC) and 5-hydroxymethylcytosine (5‑hmC) in samples using capture and detection antibodies followed by colorimetric quantification. We explored the association of participant characteristics (i.e., age, adiposity, smoking, and metal exposure) with both global DNA methylation and global DNA hydroxymethylation. Results: The Spearman’s correlation coefficient for 5‑mC and 5‑hmC levels was 0.32 (p = 0.03) at visit 1 and 0.54 (p < 0.001) at visit 3. Trends for both epigenetic modifications were consistent across potential determinants. In cross-sectional analyses, the odds ratios of methylated and hydroxymethylated DNA were 1.56 (95% CI: 0.95, 2.57) and 1.76 (95% CI: 1.07, 2.88), respectively, for the comparison of participants above and below the median percentage of dimethylarsinate. The corresponding odds ratios were 1.64 (95% CI: 1.02, 2.65) and 1.16 (95% CI: 0.70, 1.94), respectively, for the comparison of participants above and below the median cadmium level. Arsenic exposure and metabolism were consistently associated with both epigenetic markers in cross-sectional and prospective analyses. The positive correlation of 5‑mC and 5‑hmC levels was confirmed in an independent study population. Conclusions: Our findings support that both epigenetic measures are related at the population level. The consistent trends in the associations between these two epigenetic modifications and the characteristics evaluated, especially arsenic exposure and metabolism, suggest the need for understanding which of the two measures is a better biomarker for environmental epigenetic effects in future large-scale epidemiologic studies. [ABSTRACT FROM AUTHOR]

Aims and objectives To characterise a sample of 202 adult community-living long-term indwelling urinary catheter users, to describe self-care practices and catheter problems, and to explore relationships among demographics, catheter practices and problems. Background Long-term urinary catheter users have not been well studied, and persons using the device indefinitely for persistent urinary retention are likely to have different patterns of catheter practices and problems. Design The study was a cross-sectional descriptive and exploratory analysis. Methods Home interviews were conducted with catheter users who provided information by self-reported recall over the previous two months. Data were analysed by descriptive statistics and tests of association between demographics, catheter practices and catheter problems. Results The sample was widely diverse in age (19-96 years), race and medical diagnosis. Urethral catheters were used slightly more often (56%) than suprapubic (44%), for a mean of six years ( SD 7 years). Many persons were highly disabled, with 60% having difficulty in bathing, dressing, toileting and getting out of the bed; 19% also required assistance in eating. A high percentage of catheter problems were reported with: 43% experiencing leakage (bypassing of urine), 31% having had a urinary tract infection, 24% blockage of the catheter, 23% catheter-associated pain and 12% accidental dislodgment of the catheter. Treatments of catheter-related problems contributed to additional health care utilisation, including extra nurse or clinic visits, trips to the emergency department or hospitalisation. Symptoms of catheter-associated urinary tract infections were most often related to changes in the colour or character of urine or generalised symptoms. Conclusions Catheter-related problems contribute to excess morbidity and health care utilisation and costs. Relevance to clinical practice More research is needed in how to minimise catheter-associated problems in long-term catheter users. Information from this study could help inform the development of interventions in this population. [ABSTRACT FROM AUTHOR]

Abstract: Density function is a fundamental concept in data analysis. Non-parametric methods including kernel smoothing estimate are available if the data is completely observed. However, in studies such as diagnostic studies following a two-stage design the membership of some of the subjects may be missing. Simply ignoring those subjects with unknown membership is valid only in the MCAR situation. In this paper, we consider kernel smoothing estimate of the density functions, using the inverse probability approaches to address the missing values. We illustrate the approaches with simulation studies and real study data in mental health. [Copyright &y& Elsevier]

In dealing with the Two-Class classification problems, the traditional support vector machine (SVM) often cannot achieve good classification accuracy when outliers exist in the training data set. The fuzzy support vector machine (FSVM) can resolve this problem with an appropriate fuzzy membership for each data point. The effect of the outliers can be effectively reduced when the classification problem is solved. In this paper, a new fuzzy membership function is employed in the linear and nonlinear fuzzy support vector machine respectively. The fuzzy membership is calculated based on the structural information of two classes in the input space and in the feature space. This method can distinguish the support vectors and the outliers effectively. Experimental results show that this approach contributes greatly to the reduction of the effect of the outliers and significantly improves the classification accuracy and generalization. [ABSTRACT FROM AUTHOR]

Diagnostic tests are used in a wide range of behavioral, medical, psychosocial, and healthcare-related research. Test sensitivity and specificity are the most popular measures of accuracy for diagnostic tests. Available methods for analyzing longitudinal study designs assume fixed gold or reference standards and as such do not apply to studies with dynamically changing reference standards, which are especially popular in psychosocial research. In this article, we develop a novel approach to address missing data and other related issues for modeling sensitivity and specificity within such a time-varying reference standard setting. The approach is illustrated with real as well as simulated data. [ABSTRACT FROM AUTHOR]