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Sample size and statistical power are often limited in pediatric cardiology studies due to the relative infrequency of specific congenital malformations of the heart and specific circulatory physiologies. The primary aim of this study was to determine what proportion of pediatric cardiology randomized controlled trials achieve an 80% statistical power. Secondary aims included characterizing reporting habits in these studies. A systematic review was performed to identify pertinent pediatric cardiology randomized controlled trials. The following data were collected: publication year, journal, if "power" or "sample size" were mentioned if a discrete, primary endpoint was identified. Power analyses were conducted to assess if the sample size was adequate to demonstrate results at 80% power with a p-value of less than 0.05. A total of 83 pediatric cardiology randomized controlled trials were included. Of these studies, 48% mentioned "power" or "sample size" in the methods, 49% mentioned either in the results, 12% mentioned either in the discussion, and 66% mentioned either at any point in the manuscript. 63% defined a discrete, primary endpoint. 38 studies (45%) had an adequate sample size to demonstrate differences with 80% power at a p-value of less than 0.05. A majority of these are not powered to reach the conventionally accepted 80% power target. Adequately powered studies were found to be more likely to report "power" or "sample size" and have a discrete, primary endpoint.

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The literature considers children both a risk group for administering probiotic strains and one of the populations that can most benefit from it. Due to the health benefits associated to probiotic supplementation, this scope review sought to formulate a critical evaluation of how Lacticaseibacillus rhamnosus GG, carried in food and non-food matrices, and experimental design may affect the health promotion of infants and children. In this study, a literature search was conducted in three scientific databases: PubMed, Web of Science, and SciELO to retrieve research, published in English or Spanish, which administered L. rhamnosus GG to infants and children with any disease or in eutrophic condition. Three reviewers with an expert supervision screened 540 articles, published between 2001 and 2022, which were retrieved from the databases. The data extracted was compiled and shown in this scoping review. In total, was included, after criteria observation, 44 articles in this review. Intestinal disorders were the most frequent outcome in these studies (36.4%) and capsules, the most common vehicle for administering the probiotic strain (40.9%). Probiotic strain dose ranged from 105 to 1012 cfu/dose of L. rhamnosus GG and intervention length extended from one to more than 6 months. Food matrix showed health effects in 57.1% of the clinical trials and non-food matrix 46.7%, which indicates that the health-promoting effect of the probiotic GG strain may be equivalent between the two forms of delivery. However, the highly heterogeneous experimental designs prevent further analysis and a systematic review and meta-analysis is recommended to address just the outcomes of studies and achieve data homogeneity in order to determine which vehicle is the most suitable for health promoting.

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Based on case studies, in this chapter we discuss the extent to which the number and identity of quantitative trait loci (QTL) identified from genome-wide association studies (GWAS) are affected by curation and analysis of phenotypic data. The chapter demonstrates through examples the impact of (1) cleaning of outliers, and of (2) the choice of statistical method for estimating genotypic mean values of phenotypic inputs in GWAS. No cleaning of outliers resulted in the highest number of dubious QTL, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false positives and the risk of missing interesting, yet rare alleles. The choice of the statistical method to estimate genotypic mean values also affected the output of GWAS analysis, with reduced QTL overlap between methods. Using mixed models that capture spatial trends, among other features, increased the narrow-sense heritability of traits, the number of identified QTL and the overall power of GWAS analysis. Cleaning and choosing robust statistical models for estimating genotypic mean values should be included in GWAS pipelines to decrease both false positive and false negative rates of QTL detection.

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Nas próximas edições da seção de Bioestatística da revistaClinical & Biomedical Researchuma 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 PSSHealth(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 desenvolvida pela Unidade de Bioestatística do Grupo de Pesquisa e Pós-Graduação do Hospital de Clínicas de Porto Alegre, que permite calcular não apenas o tamanho de amostra, mas também o poder de um teste de hipóteses. (AU)

In the next issues ofClinical and Biomedical Research, the Biostatistics section will introduce a new series of articles addressing a very important subject for research planning: the minimum sample size to achieve the aim of a study. This series will show how to calculate sample size using PSS Health (Power and Sample Size for Health Researchers). This tool was built using R language through the Shiny package. It can be used for different types of study and is designed for health researchers by using terms and concepts commonly used in this area. PSS Health also suggests a text with information considered in the calculation to minimize problems of interpretation by the researchers. In this first article, a general overview of PSS Health will be presented. This tool, which was developed by the Research and Graduate Group Biostatistics Unit of the Hospital de Clínicas de Porto Alegre, is useful not only to calculate sample size but also to determine power of a hypothesis test. (AU)

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