We propose a multivariate regression model to handle multiple continuous bounded outcomes. We adopted the maximum likelihood approach for parameter estimation and inference. The model is specified by the product of univariate probability distributions and the correlation between the response variables is obtained through the correlation matrix of the random intercepts. For modeling continuous bounded variables on the interval [Formula: see text] we considered the beta and unit gamma distributions. The main advantage of the proposed model is that we can easily combine different marginal distributions for the response variable vector. The computational implementation is performed using Template Model Builder, which combines the Laplace approximation with automatic differentiation. Therefore, the proposed approach allows us to estimate the model parameters quickly and efficiently. We conducted a simulation study to evaluate the computational implementation and the properties of the maximum likelihood estimators under different scenarios. Moreover, we investigate the impact of distribution misspecification in the proposed model. Our model was motivated by a data set with multiple continuous bounded outcomes, which refer to the body fat percentage measured at five regions of the body. Simulation studies and data analysis showed that the proposed model provides a general and rich framework to deal with multiple continuous bounded outcomes.

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The medication possession ratio (MPR) method is commonly used for the determination of antiretroviral medication adherence. However, different ways of calculating MPR and methodological issues hinder the interpretation of the results and the reproducibility of the method. Thus, this study used three different models of MPR calculation and aimed to identify the one that best represents the situation of patient adherence. The results show that there was a statistically significant difference between the adherence rates determined by the three models, which indicates the need to specify the parameters used for calculation in the MPR method. However, the models individually were found to be related to viral suppression, but none of them had a greater effect than the other in this regard. The model that used residual medication (RM) and a fixed period of analysis allowed for a more precise identification of the number of doses that the patient used when compared to the others. Health services should avoid the application of the model using a variable analysis period. This study found that RM and the period of analysis considered are the main influencing factors in the accuracy of adherence results when the MPR method is used.