A finite mixture mixed proportion regression model for classification problems in longitudinal voting data.

Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.

A classification model for continuous responses: Identifying risk perception groups on health-related activities.

In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health-related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.

Flexible cloglog links for binomial regression models as an alternative for imbalanced medical data.

The complementary log-log link was originally introduced in 1922 to R. A. Fisher, long before the logit and probit links. While the last two links are symmetric, the complementary log-log link is an asymmetrical link without a parameter associated with it. Several asymmetrical links with an extra parameter were proposed in the literature over last few years to deal with imbalanced data in binomial regression (when one of the classes is much smaller than the other); however, these do not necessarily have the cloglog link as a special case, with the exception of the link based on the generalized extreme value distribution. In this paper, we introduce flexible cloglog links for modeling binomial regression models that include an extra parameter associated with the link that explains some unbalancing for binomial outcomes. For all cases, the cloglog is a special case or the reciprocal version loglog link is obtained. A Bayesian Markov chain Monte Carlo inference approach is developed. Simulations study to evaluate the performance of the proposed algorithm is conducted and prior sensitivity analysis for the extra parameter shows that a uniform prior is the most convenient for all models. Additionally, two applications in medical data (age at menarche and pulmonary infection) illustrate the advantages of the proposed models.

Augmented-limited regression models with an application to the study of the risk perceived using continuous scales.

Studies of risk perceived using continuous scales of [0,100] were recently introduced in psychometrics, which can be transformed to the unit interval, but the presence of zeros or ones are commonly observed. Motivated by this, we introduce a full inferential set of tools that allows for augmented and limited data modeling. We considered parameter estimation, residual analysis, influence diagnostic and model selection for zero-and/or-one augmented beta rectangular (ZOABR) regression models and their particular nested models, which is based on a new parameterization of the beta rectangular distribution. Different from other alternatives, we performed maximum-likelihood estimation using a combination of the EM algorithm (for the continuous part) and Fisher scoring algorithm (for the discrete part). Also, we perform an additional step, by considering other link functions, besides the usual logistic link, for modeling the response mean. By considering randomized quantile residuals, (local) influence diagnostics and model selection tools, we identified that the ZOABR regression model is the best one. We also conducted extensive simulations studies, which indicate that all developed tools work properly. Finally, we discuss the use of this type of models to treat psychometric data. It is worthwhile to mention that applications of the developed methods go beyond to Psychometric data. Indeed, they can be useful when the response variable in bounded, including or not the respective limits.

A new class of regression model for a bounded response with application in the study of the incidence rate of colorectal cancer.

Response variables in medical sciences are often bounded, e.g. proportions, rates or fractions of incidence of some disease. In this work, we are interested to study if some characteristics of the population, e.g. sex and race which can explain the incidence rate of colorectal cancer cases. To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution. The proposal is to obtain compound power normal distribution as a base distribution with a quantile transformation of another family of distributions with the same support and then is to study some properties of the new family. In addition, the new family is extended to regression models as an alternative to the regression model with a unit interval response. We also present inferential procedures based on the Bayesian methodology, specifically a Metropolis-Hastings algorithm is used to obtain the Bayesian estimates of parameters. An application to real data to illustrate the use of the new family is considered.

Incorporating the Q-Matrix Into Multidimensional Item Response Theory Models.

Multidimensional item response theory (MIRT) models use data from individual item responses to estimate multiple latent traits of interest, making them useful in educational and psychological measurement, among other areas. When MIRT models are applied in practice, it is not uncommon to see that some items are designed to measure all latent traits while other items may only measure one or two traits. In order to facilitate a clear expression of which items measure which traits and formulate such relationships as a math function in MIRT models, we applied the concept of the Q-matrix commonly used in diagnostic classification models to MIRT models. In this study, we introduced how to incorporate a Q-matrix into an existing MIRT model, and demonstrated benefits of the proposed hybrid model through two simulation studies and an applied study. In addition, we showed the relative ease in modeling educational and psychological data through a Bayesian approach via the NUTS algorithm.

Estimating the DINA model parameters using the No-U-Turn Sampler.

The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is accurate for all simulated scenarios. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process.

Evaluation of PCR Approaches for Detection of Bartonella bacilliformis in Blood Samples.

BACKGROUND: The lack of an effective diagnostic tool for Carrion's disease leads to misdiagnosis, wrong treatments and perpetuation of asymptomatic carriers living in endemic areas. Conventional PCR approaches have been reported as a diagnostic technique. However, the detection limit of these techniques is not clear as well as if its usefulness in low bacteriemia cases. The aim of this study was to evaluate the detection limit of 3 PCR approaches. METHODOLOGY/PRINCIPAL FINDINGS: We determined the detection limit of 3 different PCR approaches: Bartonella-specific 16S rRNA, fla and its genes. We also evaluated the viability of dry blood spots to be used as a sample transport system. Our results show that 16S rRNA PCR is the approach with a lowest detection limit, 5 CFU/µL, and thus, the best diagnostic PCR tool studied. Dry blood spots diminish the sensitivity of the assay. CONCLUSIONS/SIGNIFICANCE: From the tested PCRs, the 16S rRNA PCR-approach is the best to be used in the direct blood detection of acute cases of Carrion's disease. However its use in samples from dry blood spots results in easier management of transport samples in rural areas, a slight decrease in the sensitivity was observed. The usefulness to detect by PCR the presence of low-bacteriemic or asymptomatic carriers is doubtful, showing the need to search for new more sensible techniques.

New class of Johnson SB distributions and its associated regression model for rates and proportions.

By starting from the Johnson SB distribution pioneered by Johnson (), we propose a broad class of distributions with bounded support on the basis of the symmetric family of distributions. The new class of distributions provides a rich source of alternative distributions for analyzing univariate bounded data. A comprehensive account of the mathematical properties of the new family is provided. We briefly discuss estimation of the model parameters of the new class of distributions based on two estimation methods. Additionally, a new regression model is introduced by considering the distribution proposed in this article, which is useful for situations where the response is restricted to the standard unit interval and the regression structure involves regressors and unknown parameters. The regression model allows to model both location and dispersion effects. We define two residuals for the proposed regression model to assess departures from model assumptions as well as to detect outlying observations, and discuss some influence methods such as the local influence and generalized leverage. Finally, an application to real data is presented to show the usefulness of the new regression model.

[Validation of Body Image Relationship Scale for women with breast cancer]. / Validação do Body Image Relationship Scale para mulheres com câncer de mama.

PURPOSE: To validate the instrument Body Image Relationship Scale (BIRS) for Brazilian women with breast cancer. METHODS: The instrument was administered by trained interviewers to 139 women who used the Brazilian Unified Health System (SUS). All of them had been submitted to cancer treatments between 2006 and 2010. The instrument was validated considering internal consistency and reliability. In order to compare the techniques, the same factorial analysis as used in the original paper was carried out. RESULTS: The Spearman-Brown correlation value was 0.8, indicating high internal reliability. The Cronbach's alpha found was 0.9, indicating a high level of internal consistency. Factorial analysis showed that four items had low factorial load and no discriminatory power, and another five items were relocated to other factors. When the instrument was applied, it showed variability to that of the original instrument. CONCLUSION: The Brazilian version of the Body Image Relationship Scale (BIRS), named Escala de Relacionamento e Imagem Corporal (ERIC), showed evidence of adequate reliability and internal consistency, making this instrument suitable to be recommended for application to Brazilian women with breast cancer, despite some limitations.

Validação do Body Image Relationship Scale para mulheres com câncer de mama / Validation of Body Image Relationship Scale for women with breast cancer

ResumoOBJETIVOValidar o instrumento Body Image Relationship Scale (BIRS) em mulheres brasileiras acometidas pelo câncer de mamaMÉTODOSO instrumento foi aplicado por entrevistadoras treinadas em 139 usuárias do Sistema Único de Saúde que foram submetidas aos tratamentos do câncer entre 2006 e 2010. O instrumento foi aferido considerando-se a consistência interna e a confiabilidade. Para efeito de comparação as técnicas de análise fatorial utilizadas no artigo original foram aplicadasRESULTADOSO valor de correlação Spearman-Brown foi 0,8, o que indica alto nível de confiabilidade, e o alfa de Cronbach encontrado foi 0,9, indicando alto nível de consistência interna. A análise fatorial mostrou que quatro questões não tinham poder discriminatório e carga fatorial baixa e outras cinco foram realocadas em outros domínios. Dessa forma, foi aplicada e mostrou variabilidade semelhante ao instrumento originalCONCLUSÃOA versão brasileira do BIRS, renomeada como Escala de Relacionamento e Imagem Corporal (ERIC), apresentou evidências de adequada confiabilidade e consistência interna, o que torna esse instrumento recomendável para aplicação em mulheres brasileiras com câncer de mama, apesar de alguns poucos limites.

AbstractPURPOSETo validate the instrument Body Image Relationship Scale (BIRS) for Brazilian women with breast cancerMETHODSThe instrument was administered by trained interviewers to 139 women who used the Brazilian Unified Health System (SUS). All of them had been submitted to cancer treatments between 2006 and 2010. The instrument was validated considering internal consistency and reliability. In order to compare the techniques, the same factorial analysis as used in the original paper was carried outRESULTSThe Spearman-Brown correlation value was 0.8, indicating high internal reliability. The Cronbach's alpha found was 0.9, indicating a high level of internal consistency. Factorial analysis showed that four items had low factorial load and no discriminatory power, and another five items were relocated to other factors. When the instrument was applied, it showed variability to that of the original instrumentCONCLUSIONThe Brazilian version of the Body Image Relationship Scale (BIRS), namedEscala de Relacionamento e Imagem Corporal (ERIC), showed evidence of adequate reliability and internal consistency, making this instrument suitable to be recommended for application to Brazilian women with breast cancer, despite some limitations.

Evaluacion comparativa de tres sistemas metodologicos de estimacion de erocion hidrica en la cuenca Pajcha, cordillera del Tunari Cbba / sss

La variabilidad de las bases teoricas que fundamentan los distintos metodos de estimacion y mapeo de la erocion hidrica, condicioan la obtencion de sus resultados. Por tanto es necesario contar con lineamientos que apoyen a la seleccion de la metodologia mas adecuada en funcion de los objetivos planteados, sus requerimientos de informacion, personal y tiempo de realizacion; asi como la pertinencia y aplicabilidad de sus resultados. En este sentido el objetivo de este trabajo se centro en la evaluacion de tres metodologias de estimacion de erocion hidrica: metodo matricial, metodo IRE y metodo Silsoe; en una cuenca de alta montaña de la cordillera del Tunari, Cochabamba, Bolivia. Los resultados muestran que el metodo IRE es aplicable para la obtencion de una vision preliminar del problema; el metodo Silsoe cuantifica perdidas de suelo con la condicion de contar con datos confiables; el metodo matricial puede aportar la suficiente informacion para respaldar un proceso de planificacion de manejo de cuencas

Evaluacion comparativa de tres sistemas metodologicos de estimacion de ereosion hidrica en la cuenca Pajcha, Cordillera del Tunari Cochabamba Bolivia / sss

Los tres metodos de estimacion de erosion hidrica aplicados en la cuenca Pajcha (por lo menos en una superficie minima) la presencia de un riesgo de erosion de moderado a alto. El metodo IRE (ajustado a la cobertura vegetal) detecta que un 12.5 por ciento de la cuenca presenta un riesgo alto de erosion un 30 por ciento de riesgo moderado alto un 54 por ciento de riesgo moderado y solo un 3 por ciento de riesgo bajo. El metodo matricial cuantifica un 27 por ciento de la cuenca con un riesgo alto 37 por ciento con riesgo moderado alto 34 por ciento con riesgo moderado y un 2 por ciento de riesgo bajo. El metodo Silsoe identifica a un 4 por ciento de la cuenca con una perdida promedio anual de suelo de 2000-350 ton/ha, un 9 por ciento con una tasa de 100 a 200 ton/ha y un 87 por ciento con una tasa menor a las 10 ton/ha...