Nas próximas edições da seção de Bioestatística da revista Clinical & Biomedical Research uma nova série de artigos será publicada abordando um assunto de grande importância ao planejar uma pesquisa: o tamanho de amostra mínimo necessário para atingir os objetivos do estudo. Nessa série será apresentado como calcular o tamanho de uma amostra usando a ferramenta PSS Health (Power and Sample Size for Health Researchers), construído em linguagem R por meio do pacote Shiny, para diferentes tipos e objetivos de estudo, direcionado à pesquisadores da área da saúde, utilizando termos e conceitos comumente utilizados nesta área. Além disso, o pacote fornece uma sugestão de texto com as informações consideradas no cálculo, e como devem ser descritas, com a finalidade de minimizar problemas de interpretação por parte dos pesquisadores. Neste primeiro artigo será apresentada essa ferramenta
Background: The presence of enthesitis is associated with higher disease activity, more disability and incapacity to work and a poorer quality of life in spondyloarthritis (SpA). There is currently no consensus on which clinical score should be used to assess enthesitis in SpA. The objective of the present work was to compare the correlation of three enthesitis indices (MASES, SPARCC and LEI) with measures of disease activity and function in a heterogeneous population of patients with axial and peripheral SpA. Methods: A cross-sectional study was conducted in three Brazilian public university hospitals; patients fulfilling ASAS classification criteria for peripheral or axial SpA were recruited and measures of disease activity and function were collected and correlated to three enthesitis indices: MASES, SPARCC and LEI using Spearman's Correlation index. ROC curves were used to determine if the the enthesitis indices were useful to discriminate patients with active disease from those with inactive disease. Results: Two hundred four patients were included, 71.1% (N = 145) fulfilled ASAS criteria for axial SpA and 28.9% (N = 59) for peripheral SpA. In axial SpA, MASES performed better than LEI (p = 0.018) and equal to SPARCC (p = 0.212) regarding correlation with disease activity (BASDAI) and function (BASFI). In peripheral SpA, only MASES had a weak but statistical significant correlation with DAS28-ESR (r s 0.310 p = 0.05) and MASES had better correlation with functional measures (HAQ) than SPARCC (p = 0.034). Conclusion: In this sample composed of SpA patients with high coexistence of axial and peripheral features, MASES showed statistical significant correlation with measures of disease activity and function in both axial and peripheral SpA.
A revista do HCPA (Clinical & Biomedical Research) está reabrindo a seção de Bioestatística com o intuito de apresentar artigos explicativos, conceituais ou tutoriais, de modo a elucidar os leitores sobre os mais diversos temas estatísticos. Neste contexto, este artigo será o primeiro de uma série que tem como objetivo responder algumas das questões mais levantadas por pesquisadores da área da saúde. Começando pela Estatística Descritiva, alguns conceitos são esclarecidos e diversas referências são indicadas para o estudo do tema e para análises em SPSS ou R-project.
Off-label use of azithromycin, hydroxychloroquine, and ivermectin (the “COVID kit”) has been suggested for COVID-19 treatment in Brazil without clinical or scientific evidence of efficacy. These drugs have known adverse drug reactions (ADR). This study aimed to analyze if the sales of drugs in the “COVID kit” are correlated to the reported number of ADR after the COVID-19 pandemic began. Data was obtained from the Brazilian Health Regulatory Agency (Anvisa) website on reported sales and ADRs for azithromycin, hydroxychloroquine, and ivermectin for all Brazilian states. The period from March 2019 to February 2020 (before the pandemic) was compared to that from March 2020 to February 2021 (during the pandemic). Trend adjustment was performed for time series data and cross-correlation analysis to investigate correlation between sales and ADR within the same month (lag 0) and in the following months (lag 1 and lag 2). Spearman’s correlation coefficient was used to assess the magnitude of the correlations. After the pandemic onset, sales of all investigated drugs increased significantly (69.75% for azithromycin, 10,856,481.39% for hydroxychloroquine, and 12,291,129.32% for ivermectin). ADR levels of all medications but azithromycin were zero before the pandemic, but increased after its onset. Cross-correlation analysis was significant in lag 1 for all drugs nationwide. Spearman’s correlation was moderate for azithromycin and hydroxychloroquine but absent for ivermectin. Data must be interpreted cautiously since no active search for ADR was performed. Our results show that the increased and indiscriminate use of ”COVID kit“ during the pandemic correlates to an increased occurrence of ADRs.
The first review of the literature on the subject combination of forecasts was made in the twentieth century by Robert Clemen. After more than twenty years, several other papers have been published with new theories and applications, but no other similar review was performed. Faced with this placement, this paper aimed to review the literature on the approaches of combining forecast after the survey conducted by Clemen (1989), covering the various areas of knowledge. Thus, this paper presents the classification and analysis of 174 articles collected on the subject, describing their main characteristics. As main contributions, this paper offers: a summary of current literature on the topic; a classification of articles according to the approaches; a subdivision of items within each approach; analysis of classification and identification of the most common methods, new methods, and future research.
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