A robust regression model for bounded count health data.

Bounded count response data arise naturally in health applications. In general, the well-known beta-binomial regression model form the basis for analyzing this data, specially when we have overdispersed data. Little attention, however, has been given to the literature on the possibility of having extreme observations and overdispersed data. We propose in this work an extension of the beta-binomial regression model, named the beta-2-binomial regression model, which provides a rather flexible approach for fitting a regression model with a wide spectrum of bounded count response data sets under the presence of overdispersion, outliers, or excess of extreme observations. This distribution possesses more skewness and kurtosis than the beta-binomial model but preserves the same mean and variance form of the beta-binomial model. Additional properties of the beta-2-binomial distribution are derived including its behavior on the limits of its parametric space. A penalized maximum likelihood approach is considered to estimate parameters of this model and a residual analysis is included to assess departures from model assumptions as well as to detect outlier observations. Simulation studies, considering the robustness to outliers, are presented confirming that the beta-2-binomial regression model is a better robust alternative, in comparison with the binomial and beta-binomial regression models. We also found that the beta-2-binomial regression model outperformed the binomial and beta-binomial regression models in our applications of predicting liver cancer development in mice and the number of inappropriate days a patient spent in a hospital.

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.

[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.