The negative binomial-beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial-beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Homicide and public security indicator trends in the city of Sao Paulo between 1996 and 2008: a time-series ecological study

The scope of this paper was to analyze the association between homicides and public security indicators in Sao Paulo between 1996 and 2008, after monitoring the unemployment rate and the proportion of youths in the population. A time-series ecological study for 1996 and 2008 was conducted with Sao Paulo as the unit of analysis. Dependent variable: number of deaths by homicide per year. Main independent variables: arrest-incarceration rate, access to firearms, police activity. Data analysis was conducted using Stata. IC 10.0 software. Simple and multivariate negative binomial regression models were created. Deaths by homicide and arrest-incarceration, as well as police activity were significantly associated in simple regression analysis. Access to firearms was not significantly associated to the reduction in the number of deaths by homicide (p>0,05). After adjustment, the associations with both the public security indicators were not significant. In Sao Paulo the role of public security indicators are less important as explanatory factors for a reduction in homicide rates, after adjustment for unemployment rate and a reduction in the proportion of youths. The results reinforce the importance of socioeconomic and demographic factors for a change in the public security scenario in Sao Paulo.

Bivariate gamma-geometric law and its induced Levy process

In this article we introduce a three-parameter extension of the bivariate exponential-geometric (BEG) law (Kozubowski and Panorska, 2005) [4]. We refer to this new distribution as the bivariate gamma-geometric (BGG) law. A bivariate random vector (X, N) follows the BGG law if N has geometric distribution and X may be represented (in law) as a sum of N independent and identically distributed gamma variables, where these variables are independent of N. Statistical properties such as moment generation and characteristic functions, moments and a variance-covariance matrix are provided. The marginal and conditional laws are also studied. We show that BBG distribution is infinitely divisible, just as the BEG model is. Further, we provide alternative representations for the BGG distribution and show that it enjoys a geometric stability property. Maximum likelihood estimation and inference are discussed and a reparametrization is proposed in order to obtain orthogonality of the parameters. We present an application to a real data set where our model provides a better fit than the BEG model. Our bivariate distribution induces a bivariate Levy process with correlated gamma and negative binomial processes, which extends the bivariate Levy motion proposed by Kozubowski et al. (2008) [6]. The marginals of our Levy motion are a mixture of gamma and negative binomial processes and we named it BMixGNB motion. Basic properties such as stochastic self-similarity and the covariance matrix of the process are presented. The bivariate distribution at fixed time of our BMixGNB process is also studied and some results are derived, including a discussion about maximum likelihood estimation and inference. (C) 2012 Elsevier Inc. All rights reserved.

Molecular evolution of a malaria resistance gene (DARC) in primates

Genes involved in host-pathogen interactions are often strongly affected by positive natural selection. The Duffy antigen, coded by the Duffy antigen receptor for chemokines (DARC) gene, serves as a receptor for Plasmodium vivax in humans and for Plasmodium knowlesi in some nonhuman primates. In the majority of sub-Saharan Africans, a nucleic acid variant in GATA-1 of the gene promoter is responsible for the nonexpression of the Duffy antigen on red blood cells and consequently resistance to invasion by P. vivax. The Duffy antigen also acts as a receptor for chemokines and is expressed in red blood cells and many other tissues of the body. Because of this dual role, we sequenced a 3,000-bp region encompassing the entire DARC gene as well as part of its 5' and 3' flanking regions in a phylogenetic sample of primates and used statistical methods to evaluate the nature of selection pressures acting on the gene during its evolution. We analyzed both coding and regulatory regions of the DARC gene. The regulatory analysis showed accelerated rates of substitution at several sites near known motifs. Our tests of positive selection in the coding region using maximum likelihood by branch sites and maximum likelihood by codon sites did not yield statistically significant evidence for the action of positive selection. However, the maximum likelihood test in which the gene was subdivided into different structural regions showed that the known binding region for P. vivax/P. knowlesi is under very different selective pressures than the remainder of the gene. In fact, most of the gene appears to be under strong purifying selection, but this is not evident in the binding region. We suggest that the binding region is under the influence of two opposing selective pressures, positive selection possibly exerted by the parasite and purifying selection exerted by chemokines.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

The impact of climate on Leptospirosis in Sao Paulo, Brazil

In this work, we correlate the daily number of human leptospirosis cases with several climatic factors. We used a negative binomial model that considers hospital daily admissions due to leptospirosis as the dependent variable, and the climatic variables of daily precipitation pattern, and maximum and minimum temperature as independent variables. We calculated the monthly leptospirosis admission probabilities from the precipitation and maximum temperature variables. The month of February showed the highest probability, although values were also high during the spring months. The month of February also showed the highest number of hospital admissions. Another interesting result is that, for every 20 mm precipitation, there was an average increase of 31.5% in hospital admissions. Additionally, the relative risk of leptospirosis varied from 1.1 to 2.0 when the precipitation varied from 20 to 140 mm.

Ecological implications on the aggregation of Amblyomma fuscum (Acari: Ixodidae) on Thrichomys laurentius (Rodentia: Echimyidae), in northeastern Brazil

We investigated the Amblyomma fuscum load on a pullulating wild rodent population and the environmental and biological factors influencing the tick load on the hosts. One hundred and three individuals of Thrichomys laurentius were caught in an Atlantic forest fragment in northeastern Brazil, as part of a longitudinal survey on ticks infesting non-volant small mammals. Ticks (n = 342) were found on 45 individuals and the overall mean intensity of infestation was 7.6 ticks per infested rodent. Ticks were highly aggregated in the host population and the negative binomial distribution model provides a statistically satisfactory fit. The aggregated distribution was influenced by sex and age of the host. The microhabitat preference by T. laurentius probably increases contact opportunities between hosts and aggregated infesting stages of the ticks and represents important clues about the habitat suitability for A. fuscum.

Geopropolis from Melipona scutellaris decreases the mechanical inflammatory hypernociception by inhibiting the production of IL-1 beta and TNF-alpha

Ethnopharmacological relevance: The pharmacological activity of geopropolis collected by stingless bees (important and threatened pollinators), a product widely used in folk medicine by several communities in Brazil, especially in the Northeast Region, needs to be studied. Objective: The aim of this study was to evaluate the antinociceptive activity of Melipona scutellaris geopropolis (stingless bee) using different models of nociception. Material and methods: The antinociceptive activity of the ethanolic extract of geopropolis (EEGP) and fractions was evaluated using writhing induced by acetic acid, formalin test, carrageenan-induced hypernociception, and quantification of IL-1 beta and TNF-alpha. The chemical composition was assessed by quantification of total flavonoids and phenolic compounds. Results: EEGP and its hexane and aqueous fractions showed antinociceptive activity. Both EEGP and its aqueous fraction presented activity in the mechanical inflammatory hypernociception induced by the carrageenan model, an effect mediated by the inhibition of IL-1 beta and TNF-alpha. The chemical composition of EEGP and its hexane and aqueous fractions showed a significant presence of phenolic compounds and absence of flavonoids. Conclusion: Our data indicate that geopropolis is a natural source of bioactive substances with promising antinociceptive activity. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

ATTEMPTS TO DEFINE QUASI-INTEGRABILITY

In this paper we discuss some ideas on how to define the concept of quasi-integrability. Our ideas stem from the observation that many field theory models are "almost" integrable; i.e. they possess a large number of "almost" conserved quantities. Most of our discussion will involve a certain class of models which generalize the sine-Gordon model in (1 + 1) dimensions. As will be mentioned many field configurations of these models look like those of the integrable systems and so appear to be close to those in integrable model. We will then attempt to quantify these claims looking in particular, both analytically and numerically, at field configurations with scattering solitons. We will also discuss some preliminary results obtained in other models.

Effectiveness of a multifactorial falls prevention program in community-dwelling older people when compared to usual care: study protocol for a randomised controlled trial (Prevquedas Brazil)

Background Falling in older age is a major public health concern due to its costly and disabling consequences. However very few randomised controlled trials (RCTs) have been conducted in developing countries, in which population ageing is expected to be particularly substantial in coming years. This article describes the design of an RCT to evaluate the effectiveness of a multifactorial falls prevention program in reducing the rate of falls in community-dwelling older people. Methods/design Multicentre parallel-group RCT involving 612 community-dwelling men and women aged 60 years and over, who have fallen at least once in the previous year. Participants will be recruited in multiple settings in Sao Paulo, Brazil and will be randomly allocated to a control group or an intervention group. The usual care control group will undergo a fall risk factor assessment and be referred to their clinicians with the risk assessment report so that individual modifiable risk factors can be managed without any specific guidance. The intervention group will receive a 12-week Multifactorial Falls Prevention Program consisting of: an individualised medical management of modifiable risk factors, a group-based, supervised balance training exercise program plus an unsupervised home-based exercise program, an educational/behavioral intervention. Both groups will receive a leaflet containing general information about fall prevention strategies. Primary outcome measures will be the rate of falls and the proportion of fallers recorded by monthly falls diaries and telephone calls over a 12 month period. Secondary outcomes measures will include risk of falling, fall-related self-efficacy score, measures of balance, mobility and strength, fall-related health services use and independence with daily tasks. Data will be analysed using the intention-to-treat principle.The incidence of falls in the intervention and control groups will be calculated and compared using negative binomial regression analysis. Discussion This study is the first trial to be conducted in Brazil to evaluate the effectiveness of an intervention to prevent falls. If proven to reduce falls this study has the potential to benefit older adults and assist health care practitioners and policy makers to implement and promote effective falls prevention interventions. Trial registration ClinicalTrials.gov (NCT01698580)

The Kumaraswamy Gumbel distribution

The Gumbel distribution is perhaps the most widely applied statistical distribution for problems in engineering. We propose a generalization-referred to as the Kumaraswamy Gumbel distribution-and provide a comprehensive treatment of its structural properties. We obtain the analytical shapes of the density and hazard rate functions. We calculate explicit expressions for the moments and generating function. The variation of the skewness and kurtosis measures is examined and the asymptotic distribution of the extreme values is investigated. Explicit expressions are also derived for the moments of order statistics. The methods of maximum likelihood and parametric bootstrap and a Bayesian procedure are proposed for estimating the model parameters. We obtain the expected information matrix. An application of the new model to a real dataset illustrates the potentiality of the proposed model. Two bivariate generalizations of the model are proposed.

Universality in bibliometrics

Many discussions have enlarged the literature in Bibliometrics since the Hirsch proposal, the so called h-index. Ranking papers according to their citations, this index quantifies a researcher only by its greatest possible number of papers that are cited at least h times. A closed formula for h-index distribution that can be applied for distinct databases is not yet known. In fact, to obtain such distribution, the knowledge of citation distribution of the authors and its specificities are required. Instead of dealing with researchers randomly chosen, here we address different groups based on distinct databases. The first group is composed of physicists and biologists, with data extracted from Institute of Scientific Information (IS!). The second group is composed of computer scientists, in which data were extracted from Google-Scholar system. In this paper, we obtain a general formula for the h-index probability density function (pdf) for groups of authors by using generalized exponentials in the context of escort probability. Our analysis includes the use of several statistical methods to estimate the necessary parameters. Also an exhaustive comparison among the possible candidate distributions are used to describe the way the citations are distributed among authors. The h-index pdf should be used to classify groups of researchers from a quantitative point of view, which is meaningfully interesting to eliminate obscure qualitative methods. (C) 2011 Elsevier B.V. All rights reserved.