Multivariate Generalized Linear Mixed Models for Count Data

Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TMB package available in R. We used Poisson, negative binomial (NB), and COM-Poisson distributions. To assess the estimator properties, we conducted a simulation study considering four different sample sizes and three different correlation values for each distribution. We achieved unbiased and consistent estimators for Poisson and NB distributions; for COM-Poisson estimators were consistent, but biased, especially for dispersion, variance, and correlation parameter estimators. These models were applied to two datasets. The first concerns a sample from 30 different sites collected in Australia where the number of times each one of the 41 different ant species was registered; which results in an impressive 820 variance-covariance and 41 dispersion parameters are estimated simultaneously, let alone the regression parameters. The second is from the Australia Health Survey with 5 response variables and 5190 respondents. These datasets can be considered overdispersed by the generalized dispersion index. The COM-Poisson model overcame the other two competitors considering three goodness-of-fit indexes, AIC, BIC, and maximized log-likelihood values. As a result, it estimated parameters with smaller standard errors and a greater number of significant correlation coefficients. Therefore, the proposed model is capable of dealing with multivariate count data, either under- equi- or overdispersed responses, and measuring any kind of correlation between them taking into account the effects of the covariates

Multivariate generalized linear mixed models for underdispersed count data

Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to directly calculate from the regression model a correlation measure between the response variables. In this article, we employed the Multivariate Generalized Linear Mixed Models framework, which allows the specification of a set of response variables and calculates the correlation between them through a random effect structure that follows a multivariate normal distribution. We used the maximum likelihood estimation framework to estimate all model parameters using Laplace approximation to integrate out the random effects. The derivatives are provided by automatic differentiation. The outer maximization was made using a general-purpose algorithm such as \texttt{PORT} and \texttt{BFGS}. We delimited this problem by studying only count response variables with the following distributions: Poisson, negative binomial (NB) and COM-Poisson. The models were implemented on software \texttt{R} with package \texttt{TMB}. Besides the full specification, models with simpler structures in the covariance matrix were considered (fixed and common variance, fixed dispersion, $\rho$ set to 0). These models were applied to a dataset from the National Health and Nutrition Examination Survey, where three underdispersed response variables were measured at 1281 subjects. The COM-Poisson model full specified overcome the other two competitors considering three goodness-of-fit indexes. Therefore, the proposed model can deal with multivariate count responses and measures the correlation between them taking into account the effects of the covariates.Comment: 17 pages, 4 figures, 4 table

Oclusão duodenal após cirurgia da aorta abdominal: relato de caso Duodenal obstruction following abdominal aortic surgery: case report

A maior parte dos artigos sobre obstrução duodenal após cirurgia aórtica cita dados referentes às correções da doença aneurismática e não da doença aterosclerótica. Não obstante, é consenso que se trata de uma complicação rara, cuja incidência é menor do que 1%. Os autores relatam o caso de um paciente submetido a enxerto aorto-bifemoral que apresentou, como complicação pós-operatória, oclusão duodenal. O paciente foi tratado com reintervenção cirúrgica e uso de remendo de grande omento para síntese do retroperitônio. A revisão da literatura indica que a maioria dos casos responde bem ao tratamento conservador, e a conduta cirúrgica normalmente só é necessária quando aderências são a causa da obstrução ou quando o tratamento clínico não é satisfatório após 2 semanas.Most articles on duodenal obstruction following aortic surgery report data relative to repairs of aneurysmal disease, not atherosclerotic disease. However, duodenal obstruction is an uncommon complication, occurring in less than 1% of patients. We report a case of a patient submitted to aortobifemoral bypass reconstruction who had duodenal obstruction as postoperative complication. The patient was treated with surgical intervention and omental patching for retroperitoneal synthesis. Literature review indicates that most cases respond well to the conservative treatment, and surgical conduct is usually only required when adherences are causing the obstruction or when clinical treatment is not satisfactory after 2 weeks

Spontaneous lumbar artery bleeding in patient with Von Recklinghausen’s disease: endovascular treatment

Os sangramentos no retroperitônio são, em sua grande maioria, secundários a eventos traumáticos envolvendo grande energia cinética, com poucos relatos na literatura caracterizados como espontâneos. No presente relato, descrevemos paciente gestante, portadora de doença de Von Recklinghausen e com volumoso hematoma retroperitoneal diagnosticado durante o parto cesariano, secundário a ruptura espontânea de artéria lombar. A doença de Von Recklinghausen apresenta manifestações vasculares bem descritas, caracterizando-se principalmente por estenoses que são secundárias a tumores intramurais (proliferação das células de Schwann) e raramente dilatações aneurismáticas, assintomáticas em sua maioria. No presente caso, foi realizada a aortografia com cateterização seletiva e embolização da artéria sangrante com sucesso.Retroperitoneal bleeding is mainly due to traumatic events with a high amount of kinetic energy, with few reported cases of spontaneous events in the literature. We report on a case of a pregnant woman with Von Recklinghausen"s disease and bulky retroperitoneal hematoma diagnosed during cesarean delivery secondary to spontaneous lumbar artery rupture. Von Recklinghausen"s disease has well-described vascular manifestations, mainly characterized by stenoses related to intramural tumors (Schwann cell proliferation) and rarely asymptomatic aneurysmal dilatations. In this case, aortography was performed with successful selective catheterization and embolization of the bleeding artery