Translation, Cross-Cultural Adaptation, and Validation of the Portuguese Version of the Rotterdam Elderly Pain Observation Scale

INTRODUCTION: This study reports on the translation, cultural adaptation, and validation of a Portuguese version of the Rotterdam Elderly Pain Observation Scale (REPOS), a Dutch scale to assess pain in patients who cannot communicate, with or without dementia. METHODS: This is a multicenter study in pain and neurological units involving Brazil (clinical phase) and the Netherlands (training phase). We performed a retrospective cross-sectional, 2-staged analysis, translating and culturally adapting the REPOS to a Portuguese version (REPOS-P) and evaluating its psychometric properties. Eight health professionals were trained to observe patients with low back pain. REPOS consists of 10 behavioral items scored as present or absent after a 2-min observation. The REPOS score of ≥3 in combination with the Numerical Rating Scale (NRS) of ≥4 indicated pain. The Content Validity Index (CVI) in all items and instructions showed CVI values at their maximum. According to the higher correlation coefficient found between NRS and REPOS-P, it may be suggested that there was an adequate convergent validity. RESULTS: The REPOS-P was administered to 80 patients with a mean age of 60 years (SD 11.5). Cronbach's alpha coefficient showed a moderate internal consistency of REPOS-P (α = 0.62), which is compatible with the original study of REPOS. All health professionals reached high levels of interrater agreement within a median of 10 weeks of training, assuring reproducibility. Cohen's kappa was 0.96 (SD 0.03), and the intraclass correlation coefficient was 0.98 (SD 0.02), showing high reliability of REPOS-P scores between the trainer (researcher) and the trainees (healthcare professionals). The Pearson correlation coefficient was 0.95 (95% confidence interval 0.94–0.97), showing a significant correlation between the total scores of REPOS-P and NRS. CONCLUSION: The REPOS-P was a valuable scale for assessing elderly patients with low back pain by different healthcare professionals. Short application time, ease of use, clear instructions, and the brief training required for application were essential characteristics of REPOS-P

Use of Bayesian methods in the analysis of survival data for pacients with breast cancer in presence of censoring, cure fraction and covariates

Introdução: A maior causa de mortes no mundo é devido ao câncer, cerca de 8,2 milhões em 2012 (World Cancer Report, 2014). O câncer de mama é a forma mais comum de câncer entre as mulheres e a segunda neoplasia mais frequente, seguida do câncer de pele não melanoma, representando cerca de 25% de todos os tipos de cânceres diagnosticados. Modelos estatísticos de análise sobrevivência podem ser úteis para a identificação e compreensão de fatores de risco, fatores de prognóstico, bem como na comparação de tratamentos. Métodos: Modelos estatísticos de análise de sobrevivência foram utilizados para evidenciar fatores que afetam os tempos de sobrevida livre da doença e total de um estudo retrospectivo realizado no Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Ribeirão Preto, referente a 54 pacientes com câncer de mama localmente avançado com superexpressão do Her-2 que iniciaram a quimioterapia neoadjuvante associada com o medicamento Herceptin® (Trastuzumabe) no período de 2008 a 2012. Utilizaram-se modelos univariados com distribuição Weibull sem e com a presença de fração de cura sob o enfoque frequentista e bayesiano. Utilizou-se modelos assumindo uma estrutura de dependência entre os tempos observados baseados na distribuição exponencial bivariada de Block Basu, na distribuição geométrica bivariada de Arnold e na distribuição geométrica bivariada de Basu-Dhar. Resultados: Resultados da análise univariada sem a presença de covariáveis, o modelo mais adequado às características dos dados foi o modelo Weibull com a presença de fração de cura sob o enfoque bayesiano. Ao incorporar nos modelos as covariáveis, observou-se melhor ajuste dos modelos com fração de cura, que evidenciaram o estágio da doença como um fator que afeta a sobrevida livre da doença e total. Resultados da análise bivariada sem a presença de covariáveis estimam médias de tempo de sobrevida livre da doença para os modelos Block e Basu, Arnold e Basu-Dhar de 108, 140 e 111 meses, respectivamente e de 232, 343, 296 meses para o tempo de sobrevida total. Ao incorporar as covariáveis, os modelos evidenciam que o estágio da doença afeta a sobrevida livre da doença e total. No modelo de Arnold a covariável tipo de cirurgia também se mostrou significativa. Conclusões: Os resultados do presente estudo apresentam alternativas para a análise de sobrevivência com tempos de sobrevida na presença de fração de cura, censuras e várias covariaveis. O modelo de riscos proporcionais de Cox nem sempre se adequa às características do banco de dados estudado, sendo necessária a busca de modelos estatísticos mais adequados que produzam inferências consistentes.Introduction: The leading worldwide cause of deaths is due to cancer, about 8.2 million in 2012 (World Cancer Report, 2014). Breast cancer is the most common form of cancer among women and the second most common cancer, followed by non-melanoma skin cancer, accounting for about 25% of all diagnosed types of cancers. Statistical analysis of survival models may be useful for the identification and understanding of risk factors, prognostic factors, and the comparison treatments. Methods: Statistical lifetimes models were used to highlight the important factors affecting the disease-free times and the total lifetime about a retrospective study conducted at the Hospital das Clinicas, Faculty of Medicine, University of São Paulo, Ribeirão Preto, referring to 54 patients with locally advanced breast cancer with Her-2 overexpression who started neoadjuvant chemotherapy associated with the drug Herceptin® (Trastuzumab) in the time period ranging from years 2008 to 2012. It was used univariate models assuming Weibull distribution with and without the presence of cure fraction under the frequentist and Bayesian approaches. It was also assumed models assuming a dependence structure between the observed times based on the bivariate Block-Basu exponential distribution, on the bivariate Arnold geometric distribution and on the bivariate Basu-Dhar geometric distribution. Results: From the results of the univariate analysis without the presence of covariates, the most appropriate model for the data was the Weibull model in presence of cure rate under a Bayesian approach. By incorporating the covariates in the models, there was best fit of models with cure fraction, which showed that the stage of the disease was a factor affecting disease-free survival and overall survival. From the bivariate analysis results without the presence of covariates, the estimated means for free survival time of the disease assuming the Block- Basu, Arnold and Basu-Dhar models were respectively given by 108, 140 and 111; for the overall survival times the means were given respectively by, 232, 343, 296 months. In presence of covariates, the models showed that the stage of the disease affects the disease-free survivals and the overall survival times. Assuming the Arnold model, the covariate type of surgery also was significant. Conclusions: The results of this study present alternatives for the analysis of survival times in the presence of cure fraction, censoring and covariates. The Cox proportional hazards model not always is apropriate to the database characteristics studied, which requires the search for more suitable statistical models that produce consistent inferences

Bivariate Basu-Dhar geometric model for survival data with a cure fraction

Under a context of survival lifetime analysis, we introduce in this paper Bayesian and maximum likelihood approaches for the bivariate Basu-Dhar geometric model in the presence of covariates and a cure fraction. This distribution is useful to model bivariate discrete lifetime data. In the Bayesian estimation, posterior summaries of interest were obtained using standard Markov Chain Monte Carlo methods in the OpenBUGS software. Maximum likelihood estimates for the parameters of interest were computed using the \textquotedblleft maxLik" package of the R software. Illustrations of the proposed approaches are given for two real data sets

Determination of optimum medical cut points for continuous covariates in lifetime regression models

The estimation of optimum cut points for covariates in lifetime regression models is of great interest under a medical view. Usually the choice of covariate cut points is made in an arbitrary way following the clinical expert knowledge. In this paper, it is proposed a simple and practical Bayesian approach which could be used to different lifetime distributions under AFT (accelerated failure time) modeling approach assuming censored or uncensored data to get optimum cut points with larger prognostic effects. For the Bayesian approach, MCMC simulations are used to get estimation for the cut points under a squared error loss (SEL) function. The proposed methodology is illustrated with three medical lifetime data sets