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Populations of free-ranging dogs are still a matter of concern in developing countries. The presence of stray dogs is associated with environmental and public health consequences such as the spread of zoonotic diseases. Therefore, public health managers base the promotion of public health on sanitary measures, including the control of the free-ranging dogs’ population. In this context, it is necessary to evaluate the free-ranging dogs’ life dynamics, taking into account all characteristics of the data, including long-term survival. In long-term studies, some causes of censoring are generally falsely assumed to be independent, leading to bias neglected. Therefore, we propose a likelihood-based approach for long-term clustered survival data, which is suitable to accommodate the dependent censoring. The association between lifetimes and dependent censoring is accommodated through the conditional approach of the frailty models. The marginal distributions can be adjusted assuming Weibull or piecewise exponential distributions, respectively. A Monte Carlo Expectation–Maximization algorithm is developed to estimate the proposed estimators. The simulation study results show a small relative bias and coverage probability near to the nominal level, indicating that the proposed approach works well. Moreover, the model identifiability is assured once data has a cluster structure. Finally, we analyze the survival times of free-ranging dogs from the West Bengal, India, collected between 2010 to 2015, and conclude that survival time (death due to natural cause) is negatively correlated to dependent censoring (missing cause).

The dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrating that both regression models behave well. The simulation results point to the consistency and asymptotic unbiasedness of the developed methods. Afterwards, we adjusted both models to a simulated dataset of wild boar dispersal, including excess of zeros, right-censored observations, and two covariates: age and sex. We showed that the models were useful to extract inferences about the wild boar dispersal, correctly describing the data mimicking a situation where males disperse more than females, and age has a positive effect on the dispersal of the wild boars. These results are useful to overcome some limitations regarding inferences in zero-inflated-right-censored datasets, especially concerning the wild boar’s population. Users will be provided with an R function to run the proposed models.

In this study we introduce a likelihood-based method, via the Weibull and piecewise exponential distributions, capable of accommodating the dependence between failure and censoring times. The methodology is developed for the analysis of clustered survival data and it assumes that failure and censoring times are mutually independent conditional on a latent frailty. The dependent censoring mechanism is accounted through the frailty effect and this is accomplished by means of a key parameter accommodating the correlation between failure and censored observations. The full specification of the likelihood in our work simplifies the inference procedures with respect to Huang and Wolfe since it reduces the computation burden of working with the profile likelihood. In addition, the assumptions made for the baseline distributions lead to models with continuous survival functions. In order to carry out inferences, we devise a Monte Carlo EM algorithm. The performance of the proposed models is investigated through a simulation study. Finally, we explore a real application involving patients from the Dialysis Outcomes and Practice Patterns Study observed between 1996 and 2015.

Background: The effectiveness of evidence-based, goal-directed care to reduce losses of brain-dead potential organ donors through cardiac arrest is unclear. DONORS aimed to determine whether guiding clinical management with an evidence-based goal-directed checklist can reduce losses of potential organ donors to cardiac arrest. Methods: This open cluster randomized trial assigned adult intensive care units from hospitals across Brazil with 10 or more annual brain death reports to guide the management of potential donors with the checklist (intervention group) or to maintain usual care without the checklist (control group). Intervention group was instructed to apply a checklist with 13 clinical goals and 14 corresponding actions, every six hours, from study enrollment to organ retrieval. Consecutive potential donors between 14 and 90 years of age were included. Primary outcome was incidence of loss of brain-dead potential organ donors to cardiac arrest at the individual level. A prespecified sensitivity analysis assessed checklist adherence as an effect modifier. Findings: Among 1771 potential donors screened, 1535 were included from 63 hospitals: 31 assigned to checklist guidance (743 potential donors) and 32 to usual care (792 potential donors). Seventy (9.4%) potential donors at intervention hospitals and 117 (14.8%) at control met the primary outcome (hazard ratio [HR], 0.75; 95% confidence interval [CI], 0.53 to 1.05; P=0.09). Median checklist adherence was 79% (interquartile range, 64% to 90%). Among those with adherence greater than 79%, primary outcome rate was lower than control group (5.3% vs. 14.8%; HR, 0.45; 9% CI, 0.26 to 0.75). Among those with low adherence, primary outcome rate was comparable to the control group. Interpretation: Overall use of the checklist in preventing donor loss due to cardiac arrest was inconclusive. However, predefined subgroup analysis suggests that high adherence to the checklist may reduce donor losses to cardiac arrest. Funding: Brazilian Ministry of Health through PROADI-SUS. Declaration of Interests: The authors have no conflicts of interest to disclose. Ethics Approval Statement: In accordance with ethical principles stated in the Resolution No. 466/2012 of the Brazilian National Health Council/Ministry of Health, the Research Ethics Committee of each participating hospital approved the conduct of this study with a waiver of consent for eligible potential donors. Trial Registration: ClinicalTrials.gov number, NCT03179020 (https://clinicaltrials.gov/ct2/show/NCT03179020)

Na utilização da grande parte dos métodos apresentados na literatura para analisar dados de sobrevivência, é necessário fazer a suposição de que o mecanismo gerador da censura é não informativo, ou seja, assumir que a distribuição dos tempos de censura não depende de nenhum parâmetro da distribuição dos tempos de falha. Porém, em muitas situações, essa suposição pode ser inadequada e levar a inferências enganosas. Com o objetivo de acomodar a dependência entre os tempos de falha e os tempos de censura informativa, consideramos a abordagem fornecida pelos modelos de fragilidade, especicamente a abordagem proposta por Huang & Wolfe (2002). Primeiramente propomos uma versão completamente paramétrica ao modelo de Huang & Wolfe (2002), utilizando a distribuic¸ao Weibull para ajustar os tempos de falha e os tempos de censura informativa. Em seguida, também propomos uma versão semiparamétrica, considerando a distribuição Exponencial por Partes para modelar os tempos de falha e de censura. Para esses dois modelos mostramos as etapas da estimação via abordagens de máxima verossimilhança e bayesiana. Posteriormente, propomos uma versão bayesiana do modelo de Huang & Wolfe (2002). A censura informativa também pode inuenciar as estimativas obtidas pelos modelos para analisar dados de sobrevivência com fração de cura. Para tanto, propomos um modelo paramétrico capaz de captar a dependência entre os tempos de promoção e os tempos de censura informativa, em que empregamos a distribuição Weibull para ajustar os tempos de promoção e os tempos de censura informativa. Além desse modelo, também propomos um modelo semiparamétrico para os dados de fração de cura com censura informativa, utilizando a distribuição Exponencial por Partes para modelar os tempos de promoção e tempos de censura. Apresentamos as etapas para estimação através das abordagens de máxima verossimilhança e bayesiana. Realizamos estudos de simulação Monte Carlo, considerando três cenários: dados gerados com correlações positiva, negativa e nula entre os tempos de falha e censura, para dados com e sem fração de cura. Apresentamos uma aplicação em dados sobre mortalidade em centros de diálise renal dos Estados Unidos, cedidos pelo estudo chamado Dialysis Outcomes and Practice Patterns Study (DOPPS), utilizando os modelos propostos com censura informativa. Por último, mostramos uma aplicação em dados sobre câncer de melanoma, fornecidos pelo Programa Surveillance, Epidemiology, and End Results (SEER), empregando os modelos propostos com censura informativa e fração de cura. To use the most of the methods presented in the literature to analyze survival data, it is necessary to make the assumption that the mechanism generating the censoring is noninformative, that is, assume that the distribution of the censoring times does not depend on any parameter of the lifetime distribution. However, in many situations, this assumption can be inadequate and lead to misleading inferences. With the objective to accommodate the dependence between lifetime times and informative censoring, we consider the approach provided by the frailty models, specically the approach proposed by Huang & Wolfe (2002). First, we propose a fully parametric version of the Huang & Wolfe (2002)s model, using the Weibull distribution to adjust the lifetime times and informative censoring times. Next, we also propose a semiparametric version, considering the Piecewise Exponential distribution to model the lifetime and censoring times. For these two models, we show the estimation steps via maximum likelihood and Bayesian approaches. Later we propose a Bayesian version of the Huang & Wolfe (2002)s model. Informative censoring can also inuence the estimates obtained by the models to analyze multivariate survival data with cure rate. In order to do so, we propose a fully parametric model capable of capturing the dependence between promotion times and informative censoring times, in which we use the Weibull distribution to adjust promotion times and informative censoring times. In addition to this model, we also propose a semiparametric model for cure rate data with informative censoring, using the Piecewise Exponential distribution to model the promotion times and the censoring times. We present the steps for estimation through the maximum likelihood and Bayesian approaches. Monte Carlo simulation studies were performed considering three scenarios: data generated with a positive, negative and null correlation between lifetime and censoring times for data with and without cure rate. An application is carried out on mortality data in renal dialysis centers in the United States, provided by the study called Dialysis Outcomes and Practice Patterns Study (DOPPS), using the models proposed with informative censoring. Finally, an application is presented in data on melanoma cancer, provided by the Surveillance, Epidemiology, and End Results (SEER) program, using the proposed models with informative censoring and cure rate

The purpose of this study was to evaluate the association between apolipoprotein E (APOE) allelic frequency, serum lipoproteins and breast cancer (BC). We conducted a nested case-control study within a cohort including 47 cases and 165 controls. Polymerase chain reaction-restriction fragment length polymorphism analyses of the APOE polymorphism were performed. In general, participants with the genotype including alleles e2 and e3 tended to have lower serum triglycerides, total cholesterol and low-density lipoprotein cholesterol levels and higher high-density lipoprotein (HDL) cholesterol levels compared to participants homozygous for the e3 allele and participants heterozygous for the e3 and e4 alleles, respectively. BC patients exhibited higher mean levels of total serum cholesterol (P=0.070), dietary fat intake (P=0.020) and dietary cholesterol intake (P=0.017) compared to control subjects. The allelic distribution between the two groups revealed that the presence of the e2 allele was positively associated with the absence of BC, whereas the e4 allele was positively associated with the BC case group (P=0.019). The distribution of the APOE genotypes was not significantly different between cases and controls (P=0.172). The concomitant presence of the e2 and e4 alleles was positively associated with the absence of BC and e4/e4 homozygosity was positively associated with BC (P=0.021). Our findings suggested that APOE polymorphism plays an important role in the development of BC, particularly when associated with higher serum triglyceride levels.

While multiallelic copy number variation (mCNV) loci are a major component of genomic variation, quantifying the individual copy number of a locus and defining genotypes is challenging. Few methods exist to study how mCNV genetic diversity is apportioned within and between populations (i.e. to define the population genetic structure of mCNV). These inferences are critical in populations with a small effective size, such as Amerindians, that may not fit the Hardy–Weinberg model due to inbreeding, assortative mating, population subdivision, natural selection or a combination of these evolutionary factors. We propose a likelihood-based method that simultaneously infers mCNV allele frequencies and the population structure parameter f , which quantifies the departure of homozygosity from the Hardy–Weinberg expectation. This method is implemented in the freely available software CNVice, which also infers individual genotypes using information from both the population and from trios, if available. We studied the population genetics of five immune-related mCNV loci associated with complex diseases (beta-defensins, CCL3L1/CCL4L1 , FCGR3A , FCGR3B and FCGR2C ) in 12 traditional Native American populations and found that the population structure parameters inferred for these mCNVs are comparable to but lower than those for single nucleotide polymorphisms studied in the same populations.

In this work, the treatment of landfill leachates by a photoelectrochemical procedure is reported. When applied to untreated leachates the photochemical system was significantly hindered on account of the characteristic dark coloration of the samples. At this condition the degradation process was essentially electrochemical permitting typical color and COD removal of about 50% and 20%, respectively. When a previous chemical precipitation process was applied aiming the elimination of colored species (mainly humic substances) the decolorization and COD removal was extended to 90% and 60%, respectively. Considering the extremely complex character of the leachates and its usual resistance to conventional degradation processes the result reported here attest the high potentiality of photoelectrochemical processes to remediation of recalcitrant residues.

Copy Number Variation (CNVs) são segmentos de DNA que apresentam variações do número de cópias de uma sequência (gene), em relação ao número usual de duas cópias por indivíduo, podendo variar de um kilobase a três megabases de tamanho. E, inúmeras pesquisas já identificaram a associação de variações no número de cópias de alguns genes com diversas doenças genéticas complexas, tais como o lúpus, artrite reumatoide e diabetes do tipo 1, infecções por HIV e malária. A maioria dos métodos disponíveis para a identificação de CNVs são capazes de descrever apenas o número total de cópias de um gene ou segmento de DNA por indivíduo, deixando subjacente o número de cópias por cromossomo. Gaunt et al. (2010) desenvolveram um programa chamado CoNVEM, baseado no Algoritmo EM (Expectation-Maximization) para determinar a proporção alélica em dados haploides de CNV, supondo que os dados estão em Equilíbrio de Hardy-Weinberg (EHW). No entanto, quando os dados estão em Desequilíbrio de Hardy-Weinberg não há ferramentas estatísticas para estimarmos as proporções alélicas e genotípicas de dados de CNVs. Propomos um método capaz de estimar essas proporções quando existe endogamia na população (desequilíbrio), baseado no Algoritmo EM e na abordagem da função de verossimilhança perfilada. Os cálculos foram implementados em um programa que denominamos por CNVice (Inbreeding Coefficients Estimation for CNV data), utilizando linguagem de programação R, realizado em parceria com o Laboratório de Diversidade Genética Humana, http://ldgh.com.br/, da Universidade Federal de Minas Gerais. Copy Number Variation (CNV) are DNA segments that exhibit variations in the number of copies of a sequence (gene), in relation to the usual number of two copies per individual, ranging from one kilobase to three megabases in size. Numerous studies have identified an association of the variations in the number of copies of some genes with various complex genetic diseases such as lupus, rheumatoid arthritis and type 1 diabetes, HIV infection and malaria. Most methods available for identifying CNVs are able to describe only the total number of copies of a gene or DNA segment per individual, leaving behind the number of copies per chromosome. Gaunt et al. (2010) developed a program called CoNVEM, which is based on the EM algorithm (Expectation-Maximization) to determine the allele frequencies in haploids of CNV data, assuming the data are in Hardy-Weinberg equilibrium (HWE). However, when the data are in Hardy-Weinberg Disequilibrium there aren´t any statistical tools to estimate the proportions of allelic and genotypic data CNVs. We propose a method to estimate these proportions when there is inbreeding in the population (imbalance), based on the EM algorithm and the approach of the profiled likelihood function. The calculations have been implemented in a program that we call by CNVice (Inbreeding Coefficients Estimation for CNV data), using R programming language, developed in partnership with the Laboratory of Human Genome Diversity, http://ldgh.com.br/, of Federal University of Minas Gerais.

Gastric cancer is one of the most lethal types of cancer and its incidence varies worldwide, with the Andean region of South America showing high incidence rates. We evaluated the genetic structure of the population from Lima (Peru) and performed a case-control genetic association study to test the contribution of African, European, or Native American ancestry to risk for gastric cancer, controlling for the effect of non-genetic factors. A wide set of socioeconomic, dietary, and clinic information was collected for each participant in the study and ancestry was estimated based on 103 ancestry informative markers. Although the urban population from Lima is usually considered as mestizo (i.e., admixed from Africans, Europeans, and Native Americans), we observed a high fraction of Native American ancestry (78.4% for the cases and 74.6% for the controls) and a very low African ancestry (

Introdução: Cerca de 10% de todos os tumores são primariamente causados por mutações germinativas de alta penetrância em genes de predisposição ao câncer. Indivíduos portadores dessas mutações têm risco significativamente maior de desenvolver câncer que não portadores e estratégias de redução de risco podem ser implementadas se estas pessoas forem identificadas. Essa identificação pode ser feita pela estimativa da probabilidade de mutação através da história pessoal e familiar do probando. Objetivo: Descrever um dos métodos utilizados para estimar a probabilidade de um indivíduo ser portador ou não de uma mutação germinativa em dois genes associados a uma determinada doença mendeliana, condicionada a sua história familiar (HF). Metodologia: Foi realizada a estimativa da frequência populacional da mutação em dois genes de predisposição ao câncer de mama e ovário aplicando-se a fórmula de Bayes e o teorema da probabilidade total à história familiar do indivíduo, a probabilidade dele possuir mutação considerando determinada história familiar. Resultados e conclusão: São apresentadas e analisadas as equações utilizadas no cálculo da probabilidade de uma mutação germinativa em dois genes (BRCA1 e BRCA2) associados a um desfecho específico, que é a síndrome de predisposição ao câncer de mama e ovário. Os cálculos foram feitos para incorporar a informação de dois genes, mas as fórmulas podem ser estendidas para mais genes ou para apenas um. O modelo poderá ser aplicado à população brasileira quando a probabilidade de desenvolver câncer de mama ou ovário, dado o status genético de BRCA1 e BRCA2, para esta população for conhecida. Background: About 10% of all malignancies are primarily caused by germline, high-penetrance mutations in cancer predisposition genes. Carriers of such mutations are at an increased lifetime risk of developing multiple cancers, as compared to non-carriers and strategies for cancer risk reduction are available if these at-risk individuals are identified. This can be done by estimating the probability that a given individual is a carrier of a germline mutation, based on his/her personal and familial history of cancer. Aim: Describe one of the methods used to estimate probability of carrying a germline mutation in two genes associated with a mendelian disease according to family history. Methods: Na estimate of the populational frequency of germline mutations in two breast and ovarian cancer predisposition genes was done using the Bayes formula and the probability theorem taking into account the cancer family history of the proband. Results and conclusion: The equations used in the calculations of probability for a given mutation in two genes (BRCA1 e BRCA2) are presented and analysed in relation to a specific outcome, the hereditary breast and ovarian cancer (HBOC) syndrome. Calculations were done to incorporate the information of both genes, but the same formulas may be used for inclusion of more genes or only one gene. This model may be applied to the Brazilian population once the probability of developing breast and ovarian cancer, given a specific genetic status of BRCA1 and BRCA2 (mutation penetrance) is determined for this population.