On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Tooth loss in Brazilian middle-aged adults: multilevel effects

Objective. To examine the link between tooth loss and multilevel factors in a national sample of middle-aged adults in Brazil. Material and methods. Analyses were based on the 2003 cross-sectional national epidemiological survey of the oral health of the Brazilian population, which covered 13 431 individuals (age 35-44 years). Multistage cluster sampling was used. The dependent variable was tooth loss and the independent variables were classified according to the individual or contextual level. A multilevel negative binomial regression model was adopted. Results. The average tooth loss was 14 (standard deviation 9.5) teeth. Half of the individuals had lost 12 teeth. The contextual variables showed independent effects on tooth loss. It was found that having 9 years or more of schooling was associated with protection against tooth loss (means ratio range 0.68-0.76). Not having visited the dentist and not having visited in the last >= 3 years accounted for increases of 33.5% and 21.3%, respectively, in the risk of tooth loss (P < 0.05). The increase in tooth extraction ratio showed a strong contextual effect on increased risk of tooth loss, besides changing the effect of protective variables. Conclusions. Tooth loss in middle-aged adults has important associations with social determinants of health. This study points to the importance of the social context as the main cause of oral health injuries suffered by most middle-aged Brazilian adults.

A hands-on approach for fitting long-term survival models under the GAMLSS framework

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Yield loss caused by huanglongbing in different sweet orange cultivars in So Paulo, Brazil

Citrus huanglongbing (HLB) reduces an affected orchard`s economic life. This work aimed to characterize yield loss due to HLB for different sweet orange cultivars and determine the relationship between disease severity and yield. Disease severity and yield were assessed on 949 individual trees distributed in 11 different blocks from sweet orange cultivars Hamlin, Westin, Pera and Valencia. In each block, plants showing a range of HLB severity levels and asymptomatic plants were selected. Total yield (weight of harvested fruit), mean weight of asymptomatic and symptomatic fruit, relative yield (symptomatic tree yield/mean yield of asymptomatic trees from the same block) and relative number of fruits (fruit number from symptomatic tree/mean number of fruits from asymptomatic trees from the same block) were determined. The weight of symptomatic fruit was lower than the weight of asymptomatic fruit, but the weights of asymptomatic and symptomatic fruit were not correlated with disease severity, indicating that the effects of HLB were restricted to symptomatic branches. The relationship of the relative yield with HLB severity can be satisfactorily described by a negative exponential model. The rates of yield decrease as a function of disease severity were similar for all assessed cultivars. A relative yield (up to 19%) was observed even for trees where disease severity was 100%. The strong linear relationship between relative number of fruits per tree and the relative yield per tree suggested that the yield reduction was due primarily to early fruit drop or lack of fruit set on affected branches.

Physical disability, recent illnesses and health self-assessment in a population-based study in Sao Paulo, Brazil

Objectives. To investigate health self-assessment and to estimate the prevalence of chronic diseases and recent illnesses in people with and without physical disabilities (PD) in the state of Sao Paulo, southeastern Brazil. Study design. A Cross-sectional study comprising two population-based health surveys conducted in 2002 and 2003. Methods. A total of 8317 persons (165 with PD) were interviewed in the two studies. Variables concerning to health self-assessment; chronic disease and recent illness were compared in the people with and without PD. Negative binomial regression was used in the analysis. Results. Subjects with PD more often assessed their health as poor/very poor compared to non-disabled ones. They reported more illnesses in the 15 days prior to interview as well as more chronic diseases (skin conditions, anaemia, chronic kidney disease, stroke, depression/anxiety, migraine/headache, pulmonary diseases, hypertension, diabetes, arthritis/arthrosis/rheumatic conditions and heart disease). This higher disease prevalence can be either attributed to disability itself or be associated to gender, age and schooling. Conclusions. Subjects with PD had more recent illnesses and chronic diseases and poorer health self-assessment than non-disabled ones. Age, gender, schooling and disability have individual roles in disease development among disabled people.

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set. (C) 2008 Elsevier B.V. All rights reserved.

TLR2 Is a Negative Regulator of Th17 Cells and Tissue Pathology in a Pulmonary Model of Fungal Infection

To study the role of TLR2 in a experimental model of chronic pulmonary infection, TLR2-deficient and wild-type mice were intratracheally infected with Paracoccidioides brasiliensis, a primary fungal pathogen. Compared with control, TLR2(-/-) mice developed a less severe pulmonary infection and decreased NO synthesis. Equivalent results were detected with in vitro-infected macrophages. Unexpectedly, despite the differences in fungal loads both mouse strains showed equivalent survival times and severe pulmonary inflammatory reactions. Studies on lung-infiltrating leukocytes of TLR2(-/-) mice demonstrated an increased presence of polymorphonuclear neutrophils that control fungal loads but were associated with diminished numbers of activated CD4(+) and CD8(+) T lymphocytes. TLR2 deficiency leads to minor differences in the levels of pulmonary type 1 and type 2 cytokines, but results in increased production of KC, a CXC chemokine involved in neutrophils chemotaxis, as well as TGF-beta, IL-6, IL-23, and IL-17 skewing T cell immunity to a Th17 pattern. In addition, the preferential Th17 immunity of TLR2(-/-) mice was associated with impaired expansion of regulatory CD4(+)CD25(+)FoxP3(+) T cells. This is the first study to show that TLR2 activation controls innate and adaptive immunity to P. brasiliensis infection. TLR2 deficiency results in increased Th17 immunity associated with diminished expansion of regulatory T cells and increased lung pathology due to unrestrained inflammatory reactions. The Journal of Immunology, 2009, 183: 1279-1290.

A New Mouse Model for Marfan Syndrome Presents Phenotypic Variability Associated with the Genetic Background and Overall Levels of Fbn1 Expression

Marfan syndrome is an autosomal dominant disease of connective tissue caused by mutations in the fibrillin-1 encoding gene FBN1. Patients present cardiovascular, ocular and skeletal manifestations, and although being fully penetrant, MFS is characterized by a wide clinical variability both within and between families. Here we describe a new mouse model of MFS that recapitulates the clinical heterogeneity of the syndrome in humans. Heterozygotes for the mutant Fbn1 allele mg Delta(loxPneo), carrying the same internal deletion of exons 19-24 as the mg Delta mouse model, present defective microfibrillar deposition, emphysema, deterioration of aortic wall and kyphosis. However, the onset of a clinical phenotypes is earlier in the 129/Sv than in C57BL/6 background, indicating the existence of genetic modifiers of MFS between these two mouse strains. In addition, we characterized a wide clinical variability within the 129/Sv congenic heterozygotes, suggesting involvement of epigenetic factors in disease severity. Finally, we show a strong negative correlation between overall levels of Fbn1 expression and the severity of the phenotypes, corroborating the suggested protective role of normal fibrillin-1 in MFS pathogenesis, and supporting the development of therapies based on increasing Fbn1 expression.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Modeling volatile organic compounds (voc`s) adsorption onto cup-stacked carbon nanotubes (cscnt) using the linear driving force model

Modeling volatile organic compounds (voc`s) adsorption onto cup-stacked carbon nanotubes (cscnt) using the linear driving force model. Volatile organic compounds (VOC`s) are an important category of air pollutants and adsorption has been employed in the treatment (or simply concentration) of these compounds. The current study used an ordinary analytical methodology to evaluate the properties of a cup-stacked nanotube (CSCNT), a stacking morphology of truncated conical graphene, with large amounts of open edges on the outer surface and empty central channels. This work used a Carbotrap bearing a cup-stacked structure (composite); for comparison, Carbotrap was used as reference (without the nanotube). The retention and saturation capacities of both adsorbents to each concentration used (1, 5, 20 and 35 ppm of toluene and phenol) were evaluated. The composite performance was greater than Carbotrap; the saturation capacities for the composite was 67% higher than Carbotrap (average values). The Langmuir isotherm model was used to fit equilibrium data for both adsorbents, and a linear driving force model (LDF) was used to quantify intraparticle adsorption kinetics. LDF was suitable to describe the curves.

Effect of the immunosuppressants on hepatocyte proliferation and apoptosis in a young animal model of liver regeneration: An immunohistochemical study using tissue microarrays

Hepatocyte proliferation and apoptosis (programmed cell death) occur during the liver parenchyma regeneration and the liver size modeling is mainly controlled by hepatocyte apoptosis. The purpose of the present study was to verify the influence of immunosuppressant drugs on these phenomena by utilizing tissue microarray techniques. Thirty-six weaning rats (age 21-23 days, weight 30-50 g) were divided into six groups: control, sham, hepatectomy, hepatectomy plus solumedrol, hepatectomy plus CsA, and hepatectomy plus Tac. The animals were killed one day after hepatectomy, and the remnant livers were weighed and harvested for tissue microarray sections. Liver cell proliferation was evaluated by staining for PCNA and apoptosis was detected by the TUNEL method. It was verified that CsA promoted a decrease in the liver weight, Tac and CsA decreased the proliferation index of hepatocytes, and glucocorticoid had no significant effects. The apoptosis index was not altered by hepatectomy or immunosuppressants. Our data indicate that, in the growing rat, CsA and Tac have negative effects on hepatocyte proliferation and have no effect on the hepatocyte apoptosis.

Impact of Stent-Graft Oversizing on the Thoracic Aorta: Experimental Study in a Porcine Model

Purpose: To analyze in an experimental animal model the effect of 4 different levels of stents-graft oversizing on non-atherosclerotic aortas such as those found in young individuals who undergo stent-graft repair for traumatic aortic injuries. Methods: The diameter of the porcine thoracic aorta is similar to the aorta of young adults (18-20 mm), so 25 pigs were randomized into 5 groups: 1 control (without stent-graft) and 4 oversizing groups (A: 10%-19%, B: 20%-29%, C: 30%-39%, and D: >40%). Two types of biomechanical tests were performed on all aortas 4 weeks after endoprosthesis deployment. Results: The results of the detachment test, which analyzed the strength necessary to remove the stent-graft from the aorta, were similar in the 4 groups (A: 42 N, B: 41 N, C: 46 N, and D: 46 N). However, 2 aortas ruptured during the tests (groups C and D). The second test was performed in 3 aortic segments. Maximum shear strength, maximum stress, and maximum tension supported by the aortic wall had a negative and linear correlation with oversizing. There were significant differences in all 4 groups when compared with the control group. Strain, which reflects the elastic properties of the aortic wall, was very similar in all 4 groups, but a great difference was found when compared with the control group (p<0.0001). Conclusion: The study showed an important subacute change in the biomechanical properties of the aortic wall after implantation of an oversized endoprosthesis. This weakness of the aortic wall was confirmed by 2 ruptures during the detachment test. These results partially explain the interaction of stent-grafts with non-atherosclerotic thoracic aortas and may serve as a basis for further studies and the development of specific material to be used in vascular trauma and young patients. J Endovasc Ther. 2011; 18: 576-584

Effects of bone marrow-derived mononuclear cells on airway and lung parenchyma remodeling in a murine model of chronic allergic inflammation

We hypothesized that bone marrow-derived mononuclear cells (BMDMC) would attenuate the remodeling process in a chronic allergic inflammation model. C57BL/6 mice were assigned to two groups. In OVA, mice were sensitized and repeatedly challenged with ovalbumin. Control mice (C) received saline under the same protocol. C and OVA were further randomized to receive BMDMC (2 x 10(6)) or saline intravenously 24 h before the first challenge. BMDMC therapy reduced eosinophil infiltration, smooth muscle-specific actin expression, subepithelial fibrosis, and myocyte hypertrophy and hyperplasia, thus causing a decrease in airway hyperresponsiveness and lung mechanical parameters. BMDMC from green fluorescent protein (GFP)-transgenic mice transplanted into GFP-negative mice yielded lower engraftment in OVA. BMDMC increased insulin-like growth factor expression, but reduced interleukin-5, transforming growth factor-beta, platelet-derived growth factor, and vascular endothelial growth factor mRNA expression. In conclusion, in the present chronic allergic inflammation model, BMDMC therapy was an effective pre-treatment protocol that potentiated airway epithelial cell repair and prevented inflammatory and remodeling processes. (C) 2010 Elsevier B.V. All rights reserved.