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We propose the unit gamma mixed regression model to deal with continuous bounded variables in the context of repeated measures and clustered data. The proposed model is based on the class of generalized linear mixed models and parameter estimates are obtained based on the maximum likelihood method. The computational implementation combines automatic differentiation and the Laplace approximation (via Template Model Builder/C++) to compute the derivatives of the log-likelihood function with respect to fixed and random effects parameters. We carry out extensive simulations to check the computational implementation and to verify the properties of the maximum likelihood estimators. Our results suggest that the proposed maximum likelihood approach provides unbiased and consistent estimators for all model parameters. The proposed model was motivated by two data sets. The first concerns the body fat percentage, where the goal was to investigate the effect of covariates which were taken in the same subject. The second data set refers to a water quality index data, where the main interest was to evaluate the effect of dams on the water quality measured on power plant reservoirs. The data sets and R code are provided as supplementary material. [ABSTRACT FROM AUTHOR]

We propose a multivariate regression model to deal with multiple outcomes along with repeated measures in the context of longitudinal data analysis. Our model allows for flexible and interpretable modelling of the covariance structure within outcomes by using a linear combination of known matrices, while the generalized Kronecker product is employed to take into account the correlation between outcomes. We present maximum likelihood estimation along with extensions of the classical multivariate analysis of variance and multiple comparison hypothesis tests to deal with multivariate longitudinal data. The model and the associated multivariate hypothesis test are motivated by a prospective study conducted to compare three aesthetic eyelid surgery techniques, namely blepharoplasty, endoscopic forehead lift and endoscopic forehead lift associated with blepharoplasty. The effect of the techniques was assessed using measurements of a horizontal line through pupil centre and then three vertical lines, which go in direction to lateral canthus, middle pupil and medial canthus to the top of the brow. In this study, 30 female patients were randomly divided into three groups. Preoperative measurements were compared with postoperative measurements taken 30 days, 90 days and 10 years after the surgery. The presented multivariate model provided a better fit than its univariate counterpart. The results showed that the three surgery techniques tend to increase all considered outcomes in a long-term perspective, that is, from preoperative to 10 years postoperative evaluations. The only exception was for the outcome lateral eyebrow, for which the blepharoplasty had no significant effect. [ABSTRACT FROM AUTHOR]

After hospital discharge, workers are expected to return to work (RTW). However, the ability to return and any possible limitations after returning are unclear. Will they be able to RTW? Do they face limitations? This is a prospective cohort study with descriptive analysis aimed to investigate the RTW during 1 year following hospital discharge and to identify any limitations faced by the workers. Data collection occurred between February 2017 and July 2018 in an Internal Medicine Unit at a General Hospital. Convenience sampling was used, and participation was voluntary. Participants comprised 170 adults. Participants' RTW and limitations were followed for 1 year after discharge. During hospitalisation, 95.9% of participants planned to RTW. However, only 30% and 34.1% had returned after 1 month and 1 year respectively. Initially, 57.6% did not believe that they would present any limitation after discharge. However, 85.3% experienced restrictions after 1 month, and after 1 year this percentage rose to 88.2%. A correlation was found between young adults and RTW. The presence of limitations increased non‐RTW risk. Therefore, it is recommended that the worker role should not be merely replaced by that of a patient, but it deserves attention of the latter in order to RTW activities. The work‐return process must focus on understanding, treating and monitoring workers' limitations over time. [ABSTRACT FROM AUTHOR]

We propose a multivariate regression model to deal with multiple continuous bounded data. The proposed model is based on second-moment assumptions, only. We adopted the quasi-score and Pearson estimating functions for estimation of the regression and dispersion parameters, respectively. Thus, the proposed approach does not require a multivariate probability distribution for the variable response vector. The multivariate quasi-beta regression model can easily handle multiple continuous bounded outcomes taking into account the correlation between the response variables. Furthermore, the model allows us to analyze continuous bounded data on the interval [0, 1], including zeros and/or ones. Simulation studies were conducted to investigate the behavior of the NORmal To Anything (NORTA) algorithm and to check the properties of the estimating function estimators to deal with multiple correlated response variables generated from marginal beta distributions. The model was motivated by a data set concerning the body fat percentage, which was measured at five regions of the body and represent the response variables. We analyze each response variable separately and compare it with the fit of the multivariate proposed model. The multivariate quasi-beta regression model provides better fit than its univariate counterparts, as well as allows us to measure the correlation between response variables. Finally, we adapted diagnostic tools to the proposed model. In the supplementary material, we provide the data set and R code. [ABSTRACT FROM AUTHOR]

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form ϕ μ p (1 − μ) p , where μ , ϕ and p are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the prevalence of pre-sarcopenia and sarcopenia and their relationship with clinical variables, physical activity, quality of life, and diet in patients with heart failure with reduced left ventricular ejection fraction (HFrEF)., Methods: We performed a cross-sectional study in patients with HFrEF and matched controls. Clinical, laboratory analysis, dual-emission X-ray densitometry, handgrip strength, and physical activity level questionnaire assessments were performed. Echocardiography, quality of life, gait speed, and 24-hour nutritional recall questionnaire were also analyzed. Pre-sarcopenia and sarcopenia were defined according to the European Working Group on Sarcopenia in Older People with the cut-off points of the Foundation for the National Institute of Health., Results: 79 patients and 143 controls were enrolled. Pre-sarcopenia was found in 30.4%, and sarcopenia in 10.1% of the patients. Pre-sarcopenic patients were older and shorter, and had more fractures, higher calcemia, and creatinine ( P < 0.05). Sarcopenic patients were older and had higher creatinine and TSH ( P < 0.05). After multiple logistic regression analysis, only age was associated with pre-sarcopenia (OR: 1.046; CI 1.004-1.095; P = 0.04) and SP (OR: 1.119; CI 1.039-1.229; P = 0.008). Women with HFrEF presented higher lean mass than controls ( P < 0.001), but were weaker ( P < 0.001), while men presented lower lean mass ( P < 0.001). Low gait speed was associated with right ventricular dysfunction ( P = 0.016) and lower left ventricular ejection fraction ( P = 0.037)., Conclusion: Pre-sarcopenia and sarcopenia were associated with aging. Despite having higher lean mass, women with HFrEF were weaker. Low gait speed was associated with biventricular systolic dysfunction., Competing Interests: None., (AJCD Copyright © 2019.)