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This study evaluated the measurement invariance of the Portuguese version of the Fear of COVID-19 Scale (FCV-19S) across three countries: Brazil (South America), Mozambique (East Africa), and Portugal (Southwest Europe). A total of 8694 participants were recruited through convenience sampling (7430 Brazilians, 387 Mozambicans, and 877 Portuguese adults). The unidimensional structure of the FCV-19S fitted well with each country’s data. Multigroup confirmatory factor analysis showed that the FCV-19S was partially invariant across countries and fully invariant across gender and age groups, thus providing a solid basis for cross-group comparisons. Structural invariance tests revealed different levels of fear across countries and genders but not across age groups.

This study aimed to assess the fear of COVID-19 in the Brazilian population, validate the FCV-19S and examine the association of its scores with sociodemographic and pandemic-related variables. A total of 7430 participants were recruited in an online survey. From the factor analysis results, FCV-19S proved to be suitable, indicating a higher level of fear for women and also for those aged 18–29 years. Besides, belonging to a high-risk group and having relatives diagnosed or deceased by COVID-19 showed a positive association with fear. These findings pointed out the most vulnerable groups, which can assist in planning mental health actions.

Ranked set sampling (RSS) has been proved to be an efficient sampling design for parametric and non-parametric inference. In this paper, we consider RSS-based estimation for the two-parameter Birnb...

Objective: To map out and validate the core competencies for the training of advanced practice nurses in oncology. Methods: Exploratory-descriptive research with a quantitative approach using the Delphi technique. Initially, a matrix composed of six domains outlining 112 core competencies of the clinical nurse specialist in oncology was elaborated. The Likert scale was used to measure the degree of agreement. Data collection took place through the Google Forms® platform from February to May 2021. Data were compiled and analyzed based on expert suggestions and literature. Results: 100 competencies achieved consensus (agreement level above 85.7%) in the first round; 13 new competencies were proposed by expert members; and 125 were validated during the Delphi technique. Conclusion: The mapping and validation of core competencies will allow the development of new training models aimed at advanced practice in oncology and future educational harmonization. RESUMEN Objetivo: Mapear y validar competencias centrales para la formación del enfermero de práctica avanzada en oncología. Métodos: Investigación exploratoria-descriptiva, de abordaje cuantitativo, con utilización de la técnica Delphi. Inicialmente, elaborado una matriz compuesta por seis dominios, con 112 competencias centrales del enfermero clínico especialista en oncología. Utilizado la escala de Likert para mensurar el grado de concordancia. La recolecta de datos ocurrió mediante la plataforma Google Forms® de febrero a mayo de 2021. Los datos fueron compilados y analizados basados en las sugestiones de especialistas y la literatura. Resultados: 100 competencias obtuvieron consenso (nivel de concordancia arriba de 85,7%) en la primera rodada; 13 nuevas competencias fueron propuestas por los miembros especialistas; y 125 fueron validadas en el transcurrir de la técnica Delphi. Conclusión: El mapeo y validación de competencias centrales permitirán la elaboración de nuevos modelos de formación vueltos a la práctica avanzada en oncología y harmonización educacional futura. Resumo Objetivo: Mapear e validar as competências centrais para a formação do enfermeiro de prática avançada em oncologia. Métodos: Pesquisa exploratório-descritiva, de abordagem quantitativa, com utilização da técnica Delphi. Inicialmente, elaborou-se uma matriz composta por seis domínios, com 112 competências centrais do enfermeiro clínico especialista em oncologia. Utilizou-se a escala de Likert para mensurar o grau de concordância. A coleta de dados ocorreu por meio da plataforma Google Forms® de fevereiro a maio de 2021. Os dados foram compilados e analisados com base nas sugestões de especialistas e na literatura. Resultados: 100 competências obtiveram consenso (nível de concordância acima de 85,7%) na primeira rodada; 13 novas competências foram propostas pelos membros especialistas; e 125 foram validadas no transcorrer da técnica Delphi. Conclusão: O mapeamento e a validação de competências centrais permitirão a elaboração de novos modelos de formação voltados para a prática avançada em oncologia e harmonização educacional futura.

This study investigated how the Brazilian population perceived the risk of COVID-19, people’s behavior in response to the pandemic, and trust in the health system in case of COVID-19 infection. The sample comprised 7430 Brazilian recruited through non-probability sampling. Association analysis was performed using logistic regression. The results revealed that the economically active population and those who lived with health professionals most perceived greater susceptibility to infection. In addition, the high severity of COVID-19 was more perceived by the elderly and by those who lived with individuals in high-risk groups.

In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) curve, denoted by AUC(t). A simulation study is carried out to evaluate the performance of the AUC(t) as a tool for comparing the predictive accuracy of survival models. A dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver is also considered to illustrate the usefulness of these tools to compare survival models formulated under distinct hazards frameworks.

This study aimed to evaluate and validate the Portuguese version of the Fear of COVID-19 Scale (FCV-19S) and also to investigate the association of sociodemographic variables with the FCV-19S score...

The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.

Survival models involving frailties are commonly applied in studies where correlated event time data arise due to natural or artificial clustering. In this paper we present an application of such models in the animal breeding field. Specifically, a mixed survival model with a multivariate correlated frailty term is proposed for the analysis of data from over 3611 Brazilian Nellore cattle. The primary aim is to evaluate parental genetic effects on the trait length in days that their progeny need to gain a commercially specified standard weight gain. This trait is not measured directly but can be estimated from growth data. Results point to the importance of genetic effects and suggest that these models constitute a valuable data analysis tool for beef cattle breeding.

Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing intervalcensored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.

Effective analytical tools are highly desirable for data analysis and for making the biological link between genotypic and phenotypic measures. In family data it is important to reconcile the methods that explain the phenotypic variability through fixed genetic effects and ones that estimate variance components using classical heritability methods. Thus, in this paper, we propose a method based on added-variable plot for polygenic linear mixed models applied to genome wide association studies in family-based designs. Our goal is to be able to discriminate genetic predictor variables in effects due to random polygenic and residual components. We also propose an index to detect influential families for each predictor variable identified with genetic effect. We assess the performance of our proposed method using our own family simulated data and the Genetic Analysis Workshop 17 family simulated data.

Many important complex diseases are composed of a series of phenotypes, which makes the disease diagnosis and its genetic dissection difficult. The standard procedures to determine heritability in such complex diseases are either applied for single phenotype analyses or to compare findings across phenotypes or multidimensional reduction procedures, such as principal components analysis using all phenotypes. However each method has its own problems and the challenges are even more complex for extended family data and categorical phenotypes. In this paper, we propose a methodology to determine a scale for complex outcomes involving multiple categorical phenotypes in extended pedigrees using item response theory (IRT) models that take all categorical phenotypes into account, allowing informative comparison among individuals. An advantage of the IRT framework is that a straightforward joint heritability parameter can be estimated for categorical phenotypes. Furthermore, our methodology allows many possible extensions such as the inclusion of covariates and multiple variance components. We use Markov Chain Monte Carlo algorithm for the parameter estimation and validate our method through simulated data. As an application we consider the metabolic syndrome as the multiple phenotype disease using data from the Baependi Heart Study consisting of 1,696 individuals in 95 families. We adjust IRT models without covariates and include age and age squared as covariates. The results showed that adjusting for covariates yields a higher joint heritability (ĥ2=0.53) than without co variates (ĥ2=0.21) indicating that the covariates absorbed some of the error variance.

Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals – 138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans.

Background The purpose of this study was to estimate the genetic influences on the initiation of cigarette smoking, the persistence, quantity and age-at-onset of regular cigarette use in Brazilian families. Methods The data set consisted of 1,694 individuals enrolled in the Baependi Heart Study. The heritability and the heterogeneity in genetic and environmental variance components by gender were estimated from variance components approaches, using the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package. The mixed-effects Cox model was used for the genetic analysis of the age-at onset of regular cigarette use. Results The heritability estimates were high (> 50%) for smoking initiation and were intermediate, ranging from 23.4 to 31.9%, for smoking persistence and quantity. Significant evidence for heterogeneity in variance components by gender was observed for smoking initiation and age-at-onset of regular cigarette use. Genetic factors play an important role in the interindividual variation of these phenotypes in females, while in males there is a predominant environmental component, which could be explained by greater social influences in the initiation of tobacco use. Conclusions Significant heritabilities were observed in smoking phenotypes for both males and females from the Brazilian population. These data add to the literature and are concordant with the notion of significant biological determination in smoking behavior. Samples from the Baependi Heart Study may be valuable for the mapping of genetic loci that modulate this complex biological trait.

Background It is commonly recognized that physical activity has familial aggregation; however, the genetic influences on physical activity phenotypes are not well characterized. This study aimed to (1) estimate the heritability of physical activity traits in Brazilian families; and (2) investigate whether genetic and environmental variance components contribute differently to the expression of these phenotypes in males and females. Methods The sample that constitutes the Baependi Heart Study is comprised of 1,693 individuals in 95 Brazilian families. The phenotypes were self-reported in a questionnaire based on the WHO-MONICA instrument. Variance component approaches, implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package, were applied to estimate the heritability and to evaluate the heterogeneity of variance components by gender on the studied phenotypes. Results The heritability estimates were intermediate (35%) for weekly physical activity among non-sedentary subjects (weekly PA_NS), and low (9-14%) for sedentarism, weekly physical activity (weekly PA), and level of daily physical activity (daily PA). Significant evidence for heterogeneity in variance components by gender was observed for the sedentarism and weekly PA phenotypes. No significant gender differences in genetic or environmental variance components were observed for the weekly PA_NS trait. The daily PA phenotype was predominantly influenced by environmental factors, with larger effects in males than in females. Conclusions Heritability estimates for physical activity phenotypes in this sample of the Brazilian population were significant in both males and females, and varied from low to intermediate magnitude. Significant evidence for heterogeneity in variance components by gender was observed. These data add to the knowledge of the physical activity traits in the Brazilian study population, and are concordant with the notion of significant biological determination in active behavior.

Background In family studies, it is important to evaluate the impact of genes and environmental factors on traits of interest. In particular, the relative influences of both genes and the environment may vary in different strata of the population of interest, such as young and old individuals, or males and females. Methods In this paper, extensions of the variance components model are used to evaluate heterogeneity in the genetic and environmental variance components due to the effects of sex and age (the cutoff between young and old was 43 yrs). The data analyzed were from 81 Brazilian families (1,675 individuals) of the Baependi Family Heart Study. Results The models allowing for heterogeneity of variance components by sex suggest that genetic and environmental variances are not different in males and females for diastolic blood pressure, LDL-cholesterol, and HDL-cholesterol, independent of the covariates included in the models. However, for systolic blood pressure, fasting glucose and triglycerides, the evidence for heterogeneity was dependent on the covariates in the model. For instance, in the presence of sex and age covariates, heterogeneity in the genetic variance component was suggested for fasting glucose. But, for systolic blood pressure, there was no evidence of heterogeneity in any of the two variance components. Except for the LDL-cholesterol, models allowing for heterogeneity by age provide evidence of heterogeneity in genetic variance for triglycerides and systolic and diastolic blood pressure. There was evidence of heterogeneity in environmental variance in fasting glucose and HDL-cholesterol. Conclusions Our results suggest that heterogeneity in trait variances should not be ignored in the design and analyses of gene-finding studies involving these traits, as it may generate additional information about gene effects, and allow the investigation of more sophisticated models such as the model including sex-specific oligogenic variance components.

Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires. Programas de melhoramento de bovinos necessitam critérios objetivos para avaliar e subsequentemente melhorar o sistema de produção. Este trabalho faz uso de um modelo de curva de crescimento logístico para avaliar touros com base nos pesos de suas progênies registrados repetidamente ao longo do tempo. Os parâmetros da curva são descritos como funções lineares de efeitos fixos e aleatórios. Uma abordagem Bayesiana é considerada para a estimação dos parâmetros. Análise de dados de pesos de animais da raça Nelore mostra que os modelos de curva de crescimento com efeitos fixos e aleatórios podem ser úteis para avaliar e selecionar touros.

Background/Aims: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. Methods: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. Results: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. Conclusions: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI.

Various studies have focused on breastfeeding and the possible factors associated with early weaning. This study aimed to identify risk factors and protective factors for duration of breastfeeding in low-income families in southern Curitiba, Paraná State, Brazil. A cohort study from November 2006 to March 2007 included 118 mothers of children less than two years of age and enrolled in the TC Health Unit (USTC) located in the southern area of Curitiba. Statistical analyses using the Kaplan-Meier method and Cox regression model identified the following protective factors for breastfeeding: mother's awareness of the benefits of extended breastfeeding, exclusive feeding with mother's breast milk while in the maternity ward, and rooming-in. Factors contributing to early weaning were low birth weight, mother's work outside the home, and difficulties experienced by the mother in breastfeeding during the first days postpartum.

Statement of Problem. Titanium has been suggested as a replacement for alloys currently used in single-tooth restorations and fixed partial dentures. However, difficulties in casting have resulted in incomplete margins and discrepancies in marginal fit. Purpose. This study evaluated and compared the marginal fit of crowns fabricated from a commercially pure titanium (CP Ti) and from Ti-6Al-4V alloy with crowns fabricated from a Pd-Ag alloy that served as a control. Evaluations were performed before and after marginal refinement by electrical discharge machining (EDM). Material and Methods. Forty-five bovine teeth were prepared to receive complete cast crowns. Stone and copper-plated dies were obtained from impressions. Fifteen crowns were cast with each alloy (CP Ti, Ti-6Al-4V, and Pd-Ag). Marginal fit measurements (in micrometers) were recorded at 4 reference points on each casting with a traveling microscope. Marginal refinement with EDM was conducted on the titanium-based crowns, and measurements were repeated. Data were analyzed with the Kruskal-Wallis test, paired t test, and independent t test at a 1% probability level. Results. The Kruskal-Wallis test showed significant differences among mean values of marginal fit for the as-cast CP Ti crowns (mean [SD], 83.9 [26.1] μm) and the other groups: Ti-6Al-4V (50.8 [17.2] μm) and Pd-Ag (45.2 [10.4] μm). After EDM marginal refinement, significant differences were detected among the Ti-6Al-4V crowns (24.5 [10.9] μm) and the other 2 groups: CP Ti (50.6 [20.0] μm) and Pd-Ag (not modified by EDM). Paired t test results indicated that marginal refinement with EDM effectively improved the fit of CP Ti crowns (from 83.9 to 50.6 μm) and Ti-6Al-4V crowns (from 50.8 to 24.5 μm). However, the difference in improvement between the two groups was not significant by t test. Conclusion. Within the limitations of this study, despite the superior results for Ti-6Al-4V, both groups of titanium-based crowns had clinically acceptable marginal fits. After EDM marginal refinement, the fit of cast CP Ti and Ti-6Al-4V crowns improved significantly. (J Prosthet Dent 2002;88:467-72.)

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Estudos transversais na área da saúde usualmente possuem desfecho binário e a regressão logística é a primeira opção para responder as perguntas do pesquisador. No entanto, algumas condições levam a não adequação do modelo, em sua forma padrão, para o tratamento do desfecho binário. A presença de cura, isso é, quando sabemos que uma parcela desconhecida da população não está mais em risco de desenvolver o evento de interesse, é um exemplo deste tipo de situação. O presente trabalho foi desenvolvido na linha de pesquisa “Metodologia e estatística na pesquisa em traumatismos dentários”, parceria estabelecida, desde 2015, entre o Programa Traumatismos Dentários da Faculdade de Odontologia da UFMG (PTD FAO UFMG) e o Departamento de Estatística do ICEx UFMG. O principal interesse dos pesquisadores foi estabelecer fatores de risco para a presença de Reabsorção Radicular Externa Inflamatória (RREI). A regressão logística foi a metodologia indicada para o estudo da associação entre fatores clínicos e radiográficos, medidos na primeira consulta do paciente no PTD FAO UFMG, e o desfecho de interesse, a RREI. Entretanto, a RREI só é esperada naqueles casos em que há necrose pulpar e infecção do canal radicular, ou seja, dentes cuja cicatrização pulpar for favorável não estão sob risco de desenvolver RREI. Como esta definição não é possível na consulta inicial, ou seja, no momento da coleta dos dados, caracteriza-se a presença de fração de cura latente, e que pode levar à inadequação do modelo logístico usual. Diop et al. (2011) afirmam que o problema de cura com resposta binária pode ser encarado como um modelo ZIB (Zero-inflated Binomial). Considerando-se que na casuística do projeto, um mesmo indivíduo pode apresentar mais de um dente traumatizado, o desafio do projeto foi ajustar um modelo logístico binário com fator de cura na presença de conglomerados. Para tanto, foi utilizado a metodologia apresentada por Hall e Zhang (2004), em que os autores flexibilizam o algoritmo EM (expectation-maximization) para acomodação de mais de uma medição por indivíduo em modelos zero inflacionados. Neste trabalho, apresentamos o modelo de regressão logística com fator de cura latente, que tem a mesma forma do binomial inflacionados de zeros. Em seguida, estendemos o modelo para acomodar conglomerados, que representam um ou mais dentes por paciente, e, finalmente, apresentamos a aplicação desta metodologia para analisar a casuística da FO-UFMG. Cross-sectional studies in the health area usually have a binary outcome and logistic regression is the first option to answer the researcher’s questions. However, some conditions lead to the model not being adequate, in its standard form, for the treatment of the binary outcome. The presence of a cure, that is, when we know that an unknown portion of the population is no longer at risk of developing the event of interest, is an example of this type of situation. This work was developed in the line of research "Methodology and statistics in research on dental trauma", a partnership established since 2015 between the Dental Trauma Program of the UFMG School of Dentistry (PTD FAO UFMG) and the ICEx-Statistics Department. UFMG. The researchers’ main interest was to establish risk factors for the presence of Inflammatory External Root Resorption (IRR). Logistic regression was the methodology indicated for the study of the association between clinical and radiographic factors, measured in the patient’s first consultation at the PTD FAO UFMG, and the outcome of interest, the RREI. However, RREI is only expected in those cases in which there is pulp necrosis and root canal infection, that is, teeth whose pulp healing is favorable are not at risk of developing RREI. As this definition is not possible in the initial consultation, that is, at the time of data collection, the presence of a latent cure fraction is characterized, which can lead to the inadequacy of the usual logistic model. Diop et al. (2011) claim that the binary response healing problem can be seen as a ZIB (Zero-inflated Binomial) model. Considering that in the project’s casuistry, the same individual may have more than one traumatized tooth, the project challenge was to adjust a binary logistic model with a cure factor in the presence of conglomerates. For this purpose, the methodology presented by Hall e Zhang (2004), in which the authors make the EM algorithm more flexible to accommodate more than one measurement per individual in zero-inflated models. In this work, we present the logistic regression model with latent cure factor, which has the same shape as the zero-inflated binomial. Then, we extend the model to accommodate clusters, which represent one or more teeth per patient, and, finally, we present the application of thisCross-sectional studies in the health area usually have a binary outcome and logistic regression is the first option to answer the researcher’s questions. However, some conditions lead to the model not being adequate, in its standard form, for the treatment of the binary outcome. The presence of a cure, that is, when we know that an unknown portion of the population is no longer at risk of developing the event of interest, is an example of this type of situation. This work was developed in the line of research "Methodology and statistics in research on dental trauma", a partnership established since 2015 between the Dental Trauma Program of the UFMG School of Dentistry (PTD FAO UFMG) and the ICEx-Statistics Department. UFMG. The researchers’ main interest was to establish risk factors for the presence of Inflammatory External Root Resorption (IRR). Logistic regression was the methodology indicated for the study of the association between clinical and radiographic factors, measured in the patient’s first consultation at the PTD FAO UFMG, and the outcome of interest, the RREI. However, RREI is only expected in those cases in which there is pulp necrosis and root canal infection, that is, teeth whose pulp healing is favorable are not at risk of developing RREI. As this definition is not possible in the initial consultation, that is, at the time of data collection, the presence of a latent cure fraction is characterized, which can lead to the inadequacy of the usual logistic model. Diop et al. (2011) claim that the binary response healing problem can be seen as a ZIB (Zero-inflated Binomial) model. Considering that in the project’s casuistry, the same individual may have more than one traumatized tooth, the project challenge was to adjust a binary logistic model with a cure factor in the presence of conglomerates. For this purpose, the methodology presented by Hall e Zhang (2004), in which the authors make the EM algorithm more flexible to accommodate more than one measurement per individual in zero-inflated models. In this work, we present the logistic regression model with latent cure factor, which has the same shape as the zero-inflated binomial. Then, we extend the model to accommodate clusters, which represent one or more teeth per patient, and, finally, we present the application of this methodology to analyze the case series at FO-UFMG m

A análise de dados de sobrevivência tem sido tradicionalmente baseada no modelo de regressão de Cox (COX, 1972). No entanto, a suposição de taxas de falha proporcionais assumida para esse modelo pode não ser atendida em diversas situações práticas. Essa restrição do modelo de Cox tem gerado interesse em abordagens alternativas, dentre elas os modelos dinâmicos que permitem efeito das covariáveis variando no tempo. Neste trabalho, foram revisados os principais modelos de sobrevivência dinâmicos com estrutura aditiva e multiplicativa nos contextos não paramétrico e semiparamétrico. Métodos gráficos baseados em resíduos foram apresentados com a finalidade de avaliar a qualidade de ajuste desses modelos. Uma versão tempo-dependente da área sob a curva ROC, denotada por AUC(t), foi proposta com a finalidade de avaliar e comparar a qualidade de predição entre modelos de sobrevivência com estruturas aditiva e multiplicativa. O desempenho da AUC(t) foi avaliado por meio de um estudo de simulação. Dados de três estudos descritos na literatura foram também analisados para ilustrar ou complementar os cenários que foram considerados no estudo de simulação. De modo geral, os resultados obtidos indicaram que os métodos gráficos apresentados para avaliar a adequação dos modelos em conjunto com a AUC(t) se constituem em um conjunto de ferramentas estatísticas úteis para o próposito de avaliar modelos de sobrevivência dinâmicos nos contextos não paramétrico e semiparamétrico. Além disso, a aplicação desse conjunto de ferramentas em alguns conjuntos de dados evidenciou que se, por um lado, os modelos dinâmicos são atrativos por permitirem covariáveis tempo-dependentes, por outro lado podem não ser apropriados para todos os conjuntos de dados, tendo em vista que estimação pode apresentar restrições para alguns deles. Analysis of survival data has been traditionally based on the Cox regression model (COX, 1972). However, the proportionality of the hazards required by this model may not be attended for many practical situations. This restriction of the Cox model has generated interest in alternative approaches, among them dynamic models that allow covariates with time-varying effect. In this work, the main dynamic survival models with additive and multiplicative structures were revised under the nonparametric and semiparametric settings. Graphical methods based on residuals were presented in order to evaluate the goodness-of-fit of these models. A time-dependent version of the area under the ROC curve, denoted by AUC(t), was proposed to evaluate and compare the predictive accuracy of additive and multiplicative survival models. The performance of the AUC(t) was evaluated by means of a simulation study. Data from three studies described in the literature were also analyzed to illustrate or complement the scenarios that were considered in the simulation study. Overall, the results indicate that the graphical methods presented to assess the goodness-of-fit of the models together with the AUC(t) provide a useful set of statistics tools for the purpose of evaluating dynamic survival models in the nonparametric and semiparametric settings. Moreover, applying this set of tools in some data sets showed that on the one hand dynamic models are attractive because they allow time-dependent covariates, but on the other hand they may not be appropriate for all data sets since estimation may present restrictions for some of them.

A identificação de regiões cromossômicas, ou blocos de dependência dentro do genoma humano, que são transmitidas em conjunto para seus descendentes (haplótipos) tem sido um desafio e alvo de várias iniciativas de pesquisa, muitas delas utilizando dados de plataformas de marcadores moleculares do tipo SNP (Single Nucleotide Polymorphisms - SNPs), com alta densidade dentro do DNA humano. Este trabalho faz uso de uma modelagem estocástica de campos Markovianos de alcance variável, em uma amostra estratificada de diferentes populações, para encontrar blocos de SNPs, independentes entre si, estruturando assim o genoma em regiões ilhadas de dependência. Foram utilizados dados públicos de SNPs de diferentes populações mundiais (projeto HapMap), além de uma amostra da população brasileira. As regiões de dependência configuram janelas de influência as quais foram usadas para caracterizar as diferentes populações de acordo com sua ancestralidade e os resultados obtidos mostraram que as janelas da população brasileira têm, em média, tamanho maior, evidenciando a sua história recente de miscigenação. É também proposta uma otimização da função de verossimilhança do problema para obter as janelas de consenso maximais de todas as populações. Dada uma determinada janela de consenso, uma medida de distância apropriada para variáveis categóricas, é adotada para medir sua homogeneidade/heterogeneidade. Janelas homogêneas foram identificadas na região HLA (Human Leukocyte Antigen) do genoma, a qual está associada à resposta imunológica. O tamanho médio dessas janelas foi maior do que a média encontrada no restante do cromossomo, confirmando a alta dependência existente nesta região, considerada como bastante conservada na evolução humana. Finalmente, considerando a distribuição dos SNPs entre as populações nas janelas mais heterogêneas, a Análise de Correspondência foi aplicada na construção de um classificador capaz de determinar o percentual relativo de ancestralidade de um indivíduo, o qual, submetido à validação, obteve uma eficiência de 90% de acerto da população originária. The identification of chromosome regions, or dependency blocks in the human genome, that are transmitted together to offspring (haploids) has been a challenge and object of several research initiatives, many of them using platforms of molecular markers such as SNP (Single Nucleotide Polymorphisms), with high density inside the human DNA. This work makes use of a stochastic modeling of Markov random fields, in a stratified sample of different populations, to find SNPs blocks, independent of each other, thus structuring the genome in stranded regions of dependency. Public data from different worldwide populations were used (HapMap project), beyond a Brazilian population. The dependence regions constitute windows of influence which were used to characterize the different populations according of their ancestry and the results showed that the Brazilian populations windows have, on average, a bigger size, showing their recent history of admixture. It is also proposed an optimization of likelihood function of the problem for the maximal windows of consensus from all populations. Given a particular window of consensus, a distance measure appropriated to categorical variables, it is adopted to evaluate its homogeneity/heterogeneity. Homogeneous windows were identified within region of genome called HLA (Human Leukocyte Antigen), which is associated with the immune response. The average size of these windows was bigger than the average found in the rest of the chromosome, confirming the high dependence verified in this region, considered highly conserved in the human evolution. Finally, considering the distribution of the SNPs among the populations in the most heterogeneous windows, the Correspondence Analysis was applied to build a classifier able to determine, for a given individual, the ancestry proportion from each population considered, which, submitted to a validation, obtained a 90% accuracy of the original population.

Atualmente, um dos problemas mais importantes da Genética é a identificação de genes associados com doenças complexas. Um delineamento adequado para esta finalidade corresponde à coleta de dados de famílias e plataformas de marcadores moleculares do tipo SNP (do inglês, Single Nucleotide Polimorphism). Estas plataformas representam pontos de referência estrategicamente dispostos ao longo do genoma dos indivíduos e são de alta dimensão. A análise destes dados traz desafios analíticos como o problema de múltiplos testes e a seleção de variáveis preditoras. Nesta tese, propõe-se um critério para discriminar as variáveis preditoras genéticas em efeitos devidos ao componente aleatório poligênico e ao componente residual, sob a estrutura de um modelo linear misto. Também, considerando que o efeito individual das variáveis preditoras é esperado ser pequeno, é sugerido um método para encontrar subconjuntos ordenados destas variáveis e estudar o seu efeito simultâneo sobre a variável resposta em estudo. Neste contexto, utiliza-se a teoria associada ao Gráfico da Variável Adicionada em modelos mistos. As propostas são validadas por meio de um estudo de simulação, o qual é baseado em estruturas de famílias envolvidas no Projeto ``Corações de Baependi\" (InCor/USP), cujo objetivo é identificar genes associados a fatores de risco cardiovascular na população brasileira. Para a implementação dos procedimentos, usa-se o programa R e na geração das variáveis preditoras genéticas adota-se o aplicativo SimPed. Recently, one of the most important problems in genetics is the identification of genes associated with complex diseases. A useful design for this proposal corresponds to collect data from extended families and molecular markers platforms SNPs (Single Nucleotide polymorphism). These platforms represent points of reference strategically placed along the genome of the individuals and are high dimensional. Analysis of these data brings analytical challenges as the problem of multiple testing and selection of predictive variables. In this thesis, we propose a criterion for discriminating predictors of genetic effects due to random polygenic component and the residual component, under the framework of a linear mixed model. Also, considering that the individual effects of predictor variables is expected to be small, it is suggested a method for finding ordered subsets of these variables and study their simultaneous effect on the response variable under study. In this context, is used the theory of the added variable plot under a mixed model framework. The proposals are validated through a simulation study, which is based on structures of families involved in the Project `` Baependi Heart Study (FAPESP Process 2007/58150-7), whose objective is to identify genes associated with cardiovascular risk factors in the Brazilian population. This proposal is implemented by using the R statistical environment and for the simulation of genetic predictors is adopted the SimPed application.

Neste trabalho, trˆes modelos foram ajustados a um conjunto de dados obtido de um ensaio de controle biol´ogico para Diatraea saccharalis, uma praga comum em planta¸c˜oes de cana-de-a¸c´ucar. Usando a distribui¸c˜ao binomial como modelo de probabilidade, um ajuste adequado n˜ao pode ser obtido, devido `a superdispers˜ao gerada pela variabililidade dos dados e pelo excesso de zeros. Nesse caso, o modelo binomial inflacionado de zeros (ZIB) superdisperso ´e mais flex´ývel e eficiente para a modelagem desse tipo de dados. Entretanto, quando o interesse maior est´a sobre os valores positivos das propor¸c˜oes, pode-se utilizar o modelo binomial truncado superdisperso. Uma abordagem alternativa eficiente que foi utilizada para a modelagem desse tipo de dados foi a Bayesiana, sendo o ajuste do modelo realizado usando as t´ecnicas de simula¸c˜ao Monte Carlo em Cadeias de Markov, atrav´es do algoritmo Metropolis-Hastings e a sele¸c˜ao dos modelos foi feita usando o DIC (Deviance Information Criterion) e o fator de Bayes. Os modelos foram implementados no procedimento IML (Iteractive Matrix Linear) do programa SAS (Statistical Analysis System) e no programa WinBUGS e a convergˆencia das estimativas foi verificada atrav´es da an´alise gr´afica dos valores gerados e usando os diagn´osticos de Raftery & Lewis e de Heidelberger & Welch, implementado no m´odulo CODA do programa R. In general the standard binomial regression models do not fit well to proportion data from biological control assays, manly when there is excess of zeros and overdispersion. In this work a zero-inflated binomial model is applied to a data set obtained from a biological control assay for Diatraea saccharalis, a commom pest in sugar cane. A parasite (Trichogramma galloi) was put to parasitize 128 eggs of the Anagasta kuehniella, an economically suitable alternative host (Parra, 1997), with a variable number of female parasites (2, 4, 8,..., 128), each with 10 replicates in a completely randomized experiment. When interest is only in the positive proportion data, a model can be based on the truncated binomial distribution. A Bayesian procedure was formulated using a simulation technique (Metropolis Hastings) for estimation of the posterior parameters of interest. The convergence of the Markov Chain generated was monitored by visualization of the trace plot and using Raftery & Lewis and Heidelberg & Welch diagnostics presented in the module CODA of the software R.

Este trabalho concentra-se nos modelos de regressão aditivos dinâmicos para dados longitudinais, nas versões sugeridas por Borgan et al. (2007) e Martinussen e Scheike (1999, 2000). A metodologia é destinada a dados de eventos recorrentes que são frequentemente de interesse em estudos longitudinais envolvendo múltiplas unidades de análise. Nas duas versões é modelada a média condicional dada a história prévia em função de covariáveis fixas e tempo dependentes. Com base nos resultados assintóticos são apresentados os intervalos de confiança, as bandas de confiança e os testes de hipóteses. A bondade do ajuste é considerada por meio do uso de resíduos martingais. Duas aplicações são apresentadas, cada uma referente as duas metodologias estudadas. Os dados provêm do estudo de Serrinha realizado pelo Instituto de Saúde Coletiva da Universidade Federal da Bahia. Em cada aplicação partes diferentes dos dados são utilizados, mas com o mesmo objetivo de avaliar o efeito da suplementação periódica de vitamina A sobre a diarreia em crianças menores de 5 anos. This work focuses on the regression additive dynamic models for longitudinal data, suggested in the versions of Borgan et al. (2007) and Martinussen and Scheike (1999, 2000). The method is intended for recurrent event data of interest in longitudinal studies that involves multiple analysis units. In both versions is modeled conditional meangiven the previous history in terms of fixed and time-dependent covariates. Based on the asymptotic results are presented the confidence intervals, the confidence bands and the hypothesis testing. The goodness of fit is considered through the use of martingales residuals.Two applications are presented, each one is referring to the methodologies studied. The data comes from the studies of Serrinha's city conducted by the Public Health Institute, Federal University of Bahia. In each application different parts of the data are used, but with the same purpose of evaluating the effect of periodic supplementation from vitamin A about the diarrhea in children under 5 years old.

O estudo de doenças complexas, tais como hipertensão e glicemia, é de grande importância na área médica, pois essas doenças afetam muitas pessoas no mundo e seu padrão de variação envolve componentes ambientais, genéticos e suas possíveis interações. Para o mapeamento de genes a amostragem do genoma humano é feita por meio de plataformas de marcadores moleculares e, em geral, destacam-se duas classes de marcadores: os do tipo microsatélites e os SNPs (do inglês, Single Nucleotide Polimorphisms). Os dados de famílias são comumente analisados via modelos mistos e marcadores microsatélites de efeitos aleatórios, sendo que os estudos caso-controle com indivíduos não relacionados têm sido vinculados a dados de SNPs. Neste contexto, surge a problemática de como modelar o SNP em dados de famílias, pois o mesmo pode ser modelado como um fator fixo ou aleatório. Com a finalidade de trazer contribuições a esta discussão, um dos objetivos deste trabalho é propor um exercício de simulação e análise de dados genéticos que facilite o ensino e o entendimento de conceitos de genética e do mapeamento de genes modelados a partir de efeitos fixos ou aleatórios utilizando o software R. Além disso, na análise de dados envolvendo mapas densos de SNPs é necessário contornar o problema de múltiplos testes, e a proposta em multiestágios de Aulchenko et al. (2007) é uma alternativa de análise, na qual o efeito do SNP é modelado como um fator fixo e associado a um componente residual. Logo, surge também como desafio deste trabalho, aplicar o modelo em multiestágios para o mapeamento dos genes e discutir suas vantagens e limitações. The study of complex diseases such as hypertension and glucose is of great importance in the medical field because these diseases affect many people in the world and its pattern of variation involves environmental and genetics components and their possible interactions. For genes mapping the human genome sampling is performed by means of molecular markers platforms, generally including two kinds of markers: the type microsatellite and SNPs (Single Nucleotide Polimorphisms). The family data is commonly analyzed by mixed models and random effects microsatellite markers and the case-control studies with unrelated individuals have been linked to data from SNPs. In this context the question arises of how to model the SNP on family data because it can be modeled as a fixed or random factor. In order to bring contributions to this discussion, one of the objectives of this study is to propose a simulation exercise and analysis of genetic data to facilitate the teaching and understanding of concepts of genetics and gene mapping modeled from fixed or random effects using software R. Furthermore, analysis of data involving dense maps of SNPs is necessary to overcome the problem of multiple tests, and the proposal multistage Aulchenko et al. (2007) is an alternative analysis in which the effect of SNP is modeled as a fixed factor and associated with a residual component. So there is also a challenge of this study to apply the multistage model for the mapping of genes and discuss their advantages and limitations.

O grande desafio da Epidemiologia Genética, atualmente, é identificar, em um espaço de variáveis preditoras de alta dimensão e esparso, fatores de risco genéticos para doenças complexas. Um delineamento amostral útil nestes estudos é coletar dados de trios, que são pequenos núcleos familiares (pai e mãe, livres da doença, e filho afetado) e, em cada indivíduo, obter dados do genótipo de marcadores moleculares, sendo a plataforma de marcadores do tipo SNPs (do inglês, Single Nucleotide Polymorphism), com cerca de 1 milhão de variáveis preditoras genéticas, a mais adotada. Neste trabalho é proposto um procedimento em múltiplos estágios para identificar SNPs associados com a doença em dados de trios. A primeira etapa do procedimento é baseada em uma série de análises unilocos (para cada variável preditora), usando um teste de simetria em tabelas de contingência 2 x 2 (conhecido, em Genética, como teste TDT, do inglês, Transmission Disequilibrium Test). Em um segundo estágio da análise, os resultados destes testes são usados para construir uma estatística de somas acumuladas padronizadas (CUSUM) que permite a seleção de conjuntos de SNPs (isto é, conjuntos de variáveis preditoras), possivelmente associados com a doença. Como um terceiro passo da análise, nas regiões selecionadas no passo dois, são realizadas análises de simetria via testes exatos considerando tabelas 2 x 2 e 4 x 4 (pares de SNPs). A formulação do TDT em termos de testes de simetria é uma inovação na área de Genética e facilita a extensão do caso uniloco para o multilocos. A contribuição deste trabalho reside ainda na formulação exata do teste que é útil em situações de amostras pequenas que ocorrem com frequência em dados de trios. Neste caso inferências parciais foram realizadas a partir de decomposições apropriadas da função de verossimilhança. A modelagem do problema em termos do modelo logístico permitiu concluir que não é necessário corrigir a associação para o efeito de covariáveis avaliadas nos pais. O procedimento é implementado usando recursos dos aplicativos PLINK e R. A aplicação é realizada utilizando dados de 71 trios da população brasileira, em que os indivíduos caso (filhos) foram definidos em termos da ocorrência de uma cardiopatia e, em cada um dos 213 indivíduos, estão disponíveis dados genéticos de uma plataforma de SNPs. Currently, the great challenge of Genetic Epidemiology is to identify, in a high dimensional and sparse space of predictor variables, genetic risk factors for complex diseases. A useful sampling design in these studies is to collect data from trios, which are small nuclear families (father and mother, free from disease, and affected child), and obtain genotypic information from each individual. The molecular markers platform most commonly used for this purpose is of SNPs (Single Nucleotide Polymorphisms), with about 1 million genetic predictor variables. This work proposes a multi-stage procedure to identify SNPs associated with disease using data from trios. The first step of the procedure is based on a series of single locus analysis (for each predictor variable) using a test for symmetry in 2 x 2 contingency tables (known in genetics as TDT (Transmission Disequilibrium Test). In a second stage of the analysis, the results of these tests are used to construct a standard statistic of the cumulative sums (CUSUM), which allows the selection of sets of adjacent SNPs (ie, sets of predictor variables), possibly associated with the disease. As a third step of the analysis, in the regions selected in step two, are performed an extended analysis of symmetry considering 4 x 4 contingency tables. The TDT formulation in terms of symmetry tests is an innovation in the genetics area and facilitates the extension of the single locus analysis to the multiloci case. The contribution of this work lies in the exact formulation of the symmetry test for square contingency tables that is useful in situations of small sample sizes that often occur in data from trios. In this case, partial inferences were performed from appropriate decompositions of the likelihood function. The structural modeling of the problem in terms of logistic model allowed us to conclude that there is no need to adjust the association for data from parents, but only for the effect of covariates evaluated in each parental haplotype. The procedure is implemented using resources of the R statistical environment and Plink. The application is performed using real data from 71 trios of the Southeast Brazilian population, in which affected child was defined in terms of the occurrence of one congenital heart disease, and in each of the 213 individuals, genomic data were collected using Affymetrix SNP 6.0 platform.

Em estudos longitudinais, dados ausentes constituem um grande desafio para analise. A presente dissertação mostra como dados ausentes podem apresentar grande impacto na estimação de quantidades de interesse quando se opta pelo modelo GEE como método de analise. Esse método flexível por não requerer a especificação da distribuição da variável resposta do indivíduo apresenta estimativas válidas dos coeficientes de regressão apenas na situação MCAR, isto é, quando a perda ocorre completamente ao acaso. Como essa suposição é raramente encontrada na pratica, exploramos outro mecanismo de perda de dados. A fim de corrigir o vício nas estimativas dos coeficientes de regressão, focamos na imputação múltipla, técnica proposta por Little & Rubin (1987) e que tem recebido grande destaque na literatura. Consiste em predizer os valores ausentes de forma a obter conjuntos de dados completos que podem ser analisados por meio de métodos padrão de análise.Abordamos cinco métodos de imputação de dados: três dos quais consideram um modelo de regressão e dois utilizam alguma forma de pareamento. Além dos resultados de simulação, em que comparamos os desempenhos desses diferentes métodos de imputação, entre eles um proposto, apresentamos também uma aplicação com dados reais. Os resultados indicam que a imputação de dados é uma ferramenta adequada para remover o vício das estimativas no modelo GEE, sendo o maior ganho obtido com métodosbaseados em regressão. Missing data is a major challenge for longitudinal data analysis.This dissertation shows how missing data may have a great impact on the estimation of quantities of interest when one chooses to use the GEE model. This approach - flexible in the sense that the joint distribution of a subjects response vector does not need to be specified - yield valid estimates of the regression coefficients only with data missingcompletely at random (MCAR). Because this assumption is rarely true in practice, we explored another missing data mechanism. In order to correct the bias in regression coefficient estimates, we focus on multiple imputation, a technique proposed by Little & Rubin (1987) that has received great attention in the literature. It consists of predicting missing values in order to obtain complete data sets that can be analyzed using standard methods. We discuss five methods for imputing missing data, three of which consider a regression model and two use some form of matching. Besides the simulation results, in which we compared the performance of these imputation methods, among them one proposed, we present an application with real data. The results show that multiple imputation is an appropriate tool to remove the bias of the estimates in the GEE model, the largest gain obtained with regression-based models.

Censura intervalar resulta quando os tempos de sobrevivência não são exatamente conhecidos, sabe-se apenas que eles ocorreram dentro de um intervalo. Dados de sobrevivência agrupados são casos particulares de censura intervalar quando os indivíduos são avaliados nos mesmos intervalos de tempo, ocasionando um grande número de empates. Um procedimento comum para a análise desse tipo de dados é ignorar a natureza de censura intervalar dos dados, ou seja, tratar a variável aleatória tempo como contínua e assumir que o evento ocorreu no início, no ponto médio ou no final do intervalo e, então, usar um método padrão de análise de sobrevivência. Neste estudo, simulações de Monte Carlo, com o modelo de Weibull, foram realizadas para comparar esses três procedimentos e um método novo proposto que é uma combinação desses três métodos e é orientado pela observação do histograma do tempo versus a freqüência de cada intervalo para a decisão de qual valor a ser usado. Considera-se também a análise dos dados como censura intervalar. Os resultados mostram que analisar os dados exatamente como censura intervalar é a forma correta. Entretanto, quando a taxa de falha aumenta o ponto médio poderia ser usado. A natureza discreta dos tempos de falha deve ser reconhecida quando existe um grande número de empates. Métodos de regressão para tratar dados agrupados são apresentados por Lawless (2003) e Collett (2003), cuja estrutura é especificada em termos da probabilidade de um indivíduo falhar em um intervalo, condicionada à sua sobrevivência ao intervalo anterior. Os modelos considerados na literatura são o de riscos proporcionais de Cox ou o logístico. O modelo de Weibull é proposto, neste trabalho, como uma alternativa ao modelo de Cox para ajustar dados de sobrevivência com censura intervalar no contexto de modelos discretos. Através de simulações foram construídas as estatísticas da razão de verossimilhança e do teste escore para a discriminação entre esses dois modelos. Para ilustrar as simulações duas aplicações em dados agronômicos foram utilizadas. Interval-censored results when survival times are not exactly known, knowing only that they occur in an interval. Grouped survival data are particular cases of intervalcensored when individuals are evaluated in the same time-intervals, causing a great number of ties. A common procedure for the analysis this type of data is to ignore the nature of interval-censored data, or rather, treat the random variable time as continuous, and assume that the event occurred in the beginning, midpoint or interval end, and then use a standard method of survival analysis. In this study, Monte Carlo simulations according to Weibull model, were performed in order to compare these three procedures and a new method proposed which is a combination of the three, and is directed by the observation of time histogram versus each interval frequency in order to decide which value be used. Interval-censored data is also considered. The results show that to analyse the data exactly as interval-censored is the correct form. However, when the failure rate increase the midpoint could be used. The discrete nature of failure time must be recognized when there are a great number of ties. Regression methods to treat grouped data are presented by Lawless (2003) and Collett (2003), whose structure is specified in terms of the probability of an individual failing in an interval, conditioned to his survival to previous interval. The models considered in literature are either those of Cox proportional hazards or the logistic one. Weibull model is proposed in this study as an alternative to Cox model in order to adjust survival data with interval-censored in the context of discrete models. Through simulations were built the statistics of ratio likelihood and score test to distinguish between these two models. To illustrate the simulations two applications in agronomy data were used.