Indicadores educacionais no ensino superior brasileiro : poss?veis articula??es entre desempenho e caracter?sticas do alunado.

A discuss?o sobre aproveitamento escolar e sua rela??o com a origem social ? investigada pela sociologia da educa??o desde a segunda metade do s?culo XX. No Brasil, em espec?fico, vem se produzindo um grande volume de pesquisas sobre essa tem?tica no n?vel da educa??o b?sica, a partir de an?lises que discutem desempenho alcan?ado, verificado por meio de resultados de avalia??es educacionais de larga escala, e caracter?sticas socioecon?micas dos alunos. No entanto, sob essa perspectiva, pouco tem se pesquisado no ensino superior. Nesta linha, este artigo analisa a rela??o entre o aproveitamento acad?mico de alunos de gradua??o brasileiros e seu contexto socioecon?mico. Foi feito estudo emp?rico baseado nos dados do Exame Nacional de Desempenho dos Estudantes (ENADE), obtendo-se evid?ncias sobre as caracter?sticas que influenciam no desempenho dos alunos, tais como etnia, renda e tipo de institui??o de ensino. Vale destacar o efeito compensador que parece existir entre a elevada escolaridade dos pais e a baixa renda familiar.The relationship between social group position and educational performance has been debated by sociologists since the second half of the twentieth century. In Brazil, in particular , a large volume of research on this subject focused on the basic education level has been produced, from analyses about performance achieved, verified by results of large-scale educational assessments, and socioeconomic characteristics of the students. However, from this perspective, little has been studied in higher education. In this paper, the objective is to analyze such relationship among undergraduate Brazilian students. Through statistical analyses, based on the ?Exame Nacional de Desempenho dos Estudantes (ENADE)?, we have obtained consistent evidences about the correspondence between the social characteristics of students, such as race and family income, and their performance in the college

Um novo m?todo para aloca??o de unidade em subamostras representativas baseado em covari?veis discretas.

Em estudos experimentais nos quais se deseja verificar a efic?cia de alguma interven??o, ? usual a presen?a de grupos que sofrer?o ou n?o estas interven??es para que compara??es a respeito de fatores relacionados a estas interven??es possam ser medidos. Para garantir que tais compara??es sejam v?lidas, ? necess?rias que os grupos apresentem caracter?sticas o mais semelhantes poss?vel entre si, definidas no in?cio do estudo. Este trabalho apresenta uma nova metodologia de divis?o, dado um conjunto de dados inicial, em k subamostras representativas em rela??o aos dados iniciais, com base em covari?veis que definem as caracter?sticas desta. Os resultados obtidos constatam que a metodologia de aleatoriza??o proposta apresenta resultados satisfat?rios, principalmente se comparados com a t?cnica tradicional de amostragem aleat?ria simples. As subamostras delineadas pelo m?todo apresentam um alto grau de similaridade com a amostra original, o que possibilitar? aos estudos experimentais deste trabalho uma redu??o no vi?s de sele??o, proporcionando resultados mais satisfat?rios.In experimental studies, like clinic trials, where one wants to verify the eficacy of some intervention, the presence of different groups that will suffer the or not the interventions, so one can make future comparisons. To waranty that the comparisons will be valid, it?s necessary that the groups shows the most similar characteristics among them and the original sample. This study brings a new methodology of division of an original sample in k representative sub-samples about the original sample, based in the covariates that defines the original sample characteristics. The results demonstrate that the proposed methodology shows very satisfatory results, mainly if compared to the traditional method, the random sampling. The sub-samples defined by the new method shows a high similarity with the original sample, which will made possible experimental studies with low selection bias and reliable results

Análise sequencial usando R para monitoramento de vacinas e drogas pós-comercializadas.

Este artigo descreve alguns dos principais métodos de análise sequencial voltados ao monitoramento de eventos adversos seguidos da pós-comercialização de drogas e vacinas. No contexto usual em que o número de eventos adversos segue uma distribuição de Poisson, o método MaxSPRT é ilustrado pelo uso do pacote Sequential do software de programação estatística R.This paper is dedicated to describe some of the main methods for sequential analyses devoted to post-market drug/vaccine safety surveillance. Considering the very practical problem of having adverse events arriving according to a Poisson stochastic process, here we present the use of the Sequential package for applications of the MaxSPRT method. The Sequential package was programed in the R language

Type I error probability spending for post?market drug and vaccine safety surveillance with binomial data.

Type I error probability spending functions are commonly used for designing sequential analysis of binomial data in clinical trials, but it is also quickly emerging for near?continuous sequential analysis of post?market drug and vaccine safety surveillance. It iswell known that, for clinical trials,when the null hypothesis is not rejected, it is still important to minimize the sample size. Unlike in post?market drug and vaccine safety surveillance, that is not important. In post?market safety surveillance, specially when the surveillance involves identification of potential signals, the meaningful statistical performance measure to be minimized is the expected sample size when the null hypothesis is rejected. The present paper shows that, instead of the convex Type I error spending shape conventionally used in clinical trials, a concave shape is more indicated for post?market drug and vaccine safety surveillance. This is shown for both, continuous and group sequential analysis

Confidence intervals through sequential Monte Carlo.

Usually, confidence intervals are built through inversion of a hypothesis test. When the analytical shape of the test statistic distribution is unknown, Monte Carlo simulation can be used to construct the interval. In this direction, a sequential Monte Carlo method for interval estimation is introduced. The method produces intervals with guaranteed confidence coefficients. Because in practice one always needs to establish a truncation on the number of simulations, a simple rule of thumb is offered for choosing the number of simulations as a function of desired upper bounds for the coverage probability. As a novelty in the literature, the sequential Monte Carlo method presents equivalence with the conventional Monte Carlo test. In terms of performance, the superiority of the proposed method is illustrated for two different problems, estimation of gamma distribution means, and estimation of population sizes based on mark-recapture sampling. An example of application for real data is offered for relative risk estimation following the circular spatial scan test

On the correspondence between frequentist and Bayesian tests.

Modern theory for statistical hypothesis testing can broadly be classified as Bayesian or frequentist. Unfortunately, one can reach divergent conclusions if Bayesian and frequentist approaches are applied in parallel to analyze the same data set. This is a serious impasse since there is a lack of consensus on when to use one approach in detriment of the other. However, this conflict can be resolved. The present paper shows the existence of a perfect equivalence between Bayesian and frequentist methods for testing. Hence, Bayesian and frequentist decision rules can always be calibrated, in both directions, in order to present concordant results

Type I error probability spending for post-market drug and vaccine safety surveillancewith poisson data.

Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach suppresses the other in some sense. For Poisson stochastic processes, we prove that continuous sequential analysis is uniformly better than group sequential under a comprehensive class of statistical performance measures. Hence, optimal solutions are in the class of continuous designs. This paper also offers a pioneer study that compares classical Type I error spending functions in terms of expected number of events to signal. This was done for a number of tuning parameters scenarios. The results indicate that a log-exp shape for the Type I error spending function is the best choice in most of the evaluated scenarios

Testes Monte Carlo convencionais e sequenciais comparação dos poderes e dos tempos de execução

Exportado OPUSMade available in DSpace on 2019-08-11T23:00:06Z (GMT). No. of bitstreams: 1 dissertacaoivair.pdf: 310731 bytes, checksum: e4b0746a39cde73f8b72fcff6bd0fce3 (MD5) Previous issue date: 2A realização de um teste estatístico de hipóteses está condicionada `a distribuição de probabilidade da estatística de teste sob a hipótese nula, pois é a partir desta distribuição que se obt´em o valor-p. Em algumas situações não é possível deduzir teoricamente a distribuição da estatística de teste sob H0, e uma forma de conduzir o teste nestes casos é obter o valor-p via simulações Monte Carlo. O teste Monte Carlo convencional, ou seja, que usa um n´umero m fixo de simulações para obtenção do valor-p, pode vir a se tornar custoso computacionalmente.Uma metodologia alternativa para obter o valor-p via simulação Monte Carlo, sem ser preciso fixar o número de simulações, é o testesequencial. Esta abordagem faz com que o tempo de execução do teste seja esporadicamente reduzido em relação ao teste convencional.Este trabalho tem dois objetivos principais: reduzir o tempo de execuçãodo teste sequencial de Monte Carlo sem que isso afete seu poder; comparar o poder do teste MC sequencial com o do MC convencional. Assim, mostrou-se que é possível estabelecer um máximo para o tempo de execução do método sequencial, uma vez que o poder se torna constante a partir de um certo número de simulações. Mostrou-se também que é sempre melhor optar pelo método sequencial em substituição ao método convencional, pois existe um critério de aplicação do sequencial que garante equivalência de poder entreambos e gera esporadicamente tempos de execução inferiores.Para um teste sequencial genérico, foram estabelecidas cotas para as diferenças entre os poderes deste e o do convencional. Por exemplo, para o procedimento sequencial que é, no mínimo, 3.3 vezes mais rápido que o convencional com m=999, a cota superior teórica para a perda de poder é aproximadamente 0.16, mas uma análise da curva de poder destes testes mostra que, na prática, esta perda é bem menor. Um procedimento sequencial, que tem tempo de execução no mínimo 2 vezes inferior, apresenta cota superior teórica, para a perda de poder, em torno de 0.092, mostrando que a redução de poder ao se utilizar o método sequencial é pequena, mesmo para situações em que seu tempo de execução é substancialmente inferior ao do convencional.Obtivemos também cotas para a diferença de poder entre estes testes Monte Carlo e o teste exat

Composite sequential Monte Carlo test for post-market vaccine safety surveillance.

Group sequential hypothesis testing is now widely used to analyze prospective data. If Monte Carlo simulation is used to construct the signaling threshold, the challenge is how to manage the type I error probability for each one of the multiple tests without losing control on the overall significance level. This paper introduces a valid method for a true management of the alpha spending at each one of a sequence of Monte Carlo tests. The method also enables the use of a sequential simulation strategy for each Monte Carlo test, which is useful for saving computational execution time. Thus, the proposed procedure allows for sequential Monte Carlo test in sequential analysis, and this is the reason that it is called ‘composite sequential’ test. An upper bound for the potential power losses from the proposed method is deduced. The composite sequential design is illustrated through an application for post-market vaccine safety surveillance data

Continuous versus group sequential analysis for post-market drug and vaccine safety surveillance.

The use of sequential statistical analysis for post-market drug safety surveillance is quickly emerging. Bothcontinuous and group sequential analysis have been used, but consensus is lacking as to when to use which approach. Wecompare the statistical performance of continuous and group sequential analysis in terms of type I error probability; statisticalpower; expected time to signal when the null hypothesis is rejected; and the sample size required to end surveillance withoutrejecting the null. We present a mathematical proposition to show that for any group sequential design there always existsa continuous sequential design that is uniformly better. As a consequence, it is shown that more frequent testing is alwaysbetter. Additionally, for a Poisson based probability model and a flat rejection boundary in terms of the log likelihood ratio,we compare the performance of various continuous and group sequential designs. Using exact calculations, we found that, forthe parameter settings used, there is always a continuous design with shorter expected time to signal than t he best groupdesign. The two key conclusions from this article are (i) that any post-market safety surveillance system should attempt toobtain data as frequently as possible, and (ii) that sequential testing should always be performed when new data arriveswithout deliberately waiting for additional data