RESUMO
Metanálise consiste em um conjunto de técnicas estatísticas que visa integrar os resultados de dois ou mais estudos primários. Ela permite produzir estimativas pontuais e intervalares de algum parâmetro populacional, geralmente uma medida de tamanho de efeito. Este artigo tem como objetivo apresentar conceitos fundamentais sobre metanálise e suas aplicações para psicólogos e estudantes de psicologia. O artigo: (1) introduz a lógica da metanálise, seus potenciais e as críticas a ela endereçadas; (2) apresenta dois modelos de metanálise comumente usados por pesquisadores; e (3) aborda dois tópicos importantes para a interpretação correta dos resultados: heterogeneidade e análise de subgrupos. Um exemplo fictício ilustra os conceitos ao longo do artigo. Os Materiais Suplementares contêm equações dos modelos apresentados no texto, resultados comentados de uma síntese metanalítica, código na linguagem R para reproduzir resultados e figuras desse artigo e uma breve lista comentada de fontes adicionais sobre metanálise. (AU)
Meta-analysis consists of a set of statistical techniques that aims to combine the results of two or more primary studies. It enables the calculation of point and interval estimates of some population parameter, usually a measure of effect size. The aim of this article is to introduce fundamental concepts of meta-analysis and its applications for psychologists and psychology students. The article: (1) introduces the logic of meta-analysis, its uses and common criticisms levied against it; (2) presents two computational models of meta-analysis commonly used by researchers; and (3) addresses two issues associated with the correct interpretation of results from meta-analyses: heterogeneity and subgroup analysis. A worked example illustrates the concepts throughout the article. The Supplementary Materials contain a worked example of the models presented in the text, a script in R language that allows the reader to reproduce the results, and a commented list of additional sources. (AU)
El metanálisis consiste en un conjunto de técnicas estadísticas que tiene como objetivo integrar los resultados de dos o más estudios primarios. Permite producir estimaciones puntuales y de intervalo de algún parámetro de población, generalmente una medida del tamaño del efecto. Este artículo presenta conceptos fundamentales sobre el metanálisis y sus aplicaciones para psicólogos y estudiantes de psicología. El artículo: (1) introduce la lógica del metanálisis, sus potencialidades y las críticas que se le dirigen; (2) presenta dos modelos de metanálisis comúnmente utilizados por los investigadores; y (3) aborda dos temas importantes para la correcta interpretación de los resultados: heterogeneidad y análisis de subgrupos. Un ejemplo ficticio ilustra los conceptos a lo largo del artículo. Los Materiales Suplementarios contienen ecuaciones de los modelos presentados en el texto, resultados comentados de una síntesis metanalítica, código en el lenguaje R para reproducir los resultados y las figuras de este artículo, y una breve lista comentada de fuentes adicionales. (AU)
Assuntos
Revisões Sistemáticas como Assunto , Simulação por Computador , Revisão , Metanálise , Estatística , Estudos de Avaliação como AssuntoRESUMO
Systematic reviews involve the application of scientific methods to reduce bias in review of literature. The key components of a systematic review are a well-defined research question, comprehensive literature search to identify all studies that potentially address the question, systematic assembly of the studies that answer the question, critical appraisal of the methodological quality of the included studies, data extraction and analysis (with and without statistics), and considerations towards applicability of the evidence generated in a systematic review. These key features can be remembered as six ‘A’; Ask, Access, Assimilate, Appraise, Analyze and Apply. Meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review. The graphical output of meta-analysis is a forest plot which provides information on individual studies and the pooled effect. Systematic reviews of literature can be undertaken for all types of questions, and all types of study designs. This article highlights the key features of systematic reviews, and is designed to help readers understand and interpret them. It can also help to serve as a beginner’s guide for both users and producers of systematic reviews and to appreciate some of the methodological issues.
RESUMO
@#AIM: To explore the current situation of myopia among children and adolescents in Mengzi city and the possible influencing factors of myopia.METHODS: A multi-stage cluster sampling method was used to select students in 7 schools(2 primary schools, 2 junior high schools, 2 high schools, and 1 vocational high school)in Mengzi city, Yunnan Province in October 2019. A total of 1 837 students were selected for questionnaire surveys, and examination of distance visual acuity and noncycloplegic autorefraction. There were 1 622 valid questionnaires were finally collected after checking the integrity and rationality of the questionnaires. RESULTS: The prevalence of myopia among primary and secondary students in Mengzi city in 2019 was 61.34%. The prevalence of myopia in girls(71.36%)was higher than that in boys(50.45%), and the prevalence of myopia in Han nationality(70.19%)was higher than that in ethnic minorities(57.70%). With the grade growth, the prevalence of myopia showed an upward trend. Multivariate Logistic regression analysis showed that the risk factors of myopia were female(<i>OR</i>=2.308), Han nationality(<i>OR</i>=1.712), higher learning stage(junior high school: <i>OR</i>=1.579, high school: <i>OR</i>=5.538), the time of doing homework daily in the past 1wk(1-<2h: <i>OR</i>=1.456, 2-<3h: <i>OR</i>=1.514, ≥3h: <i>OR</i>=1.901), occasionally or never keep your eyes more than one foot away from a book while reading and writing(<i>OR</i>=1.741), insufficient sleep(<i>OR</i>=1.585), parental myopia(<i>OR</i>=2.191).CONCLUSION:The prevalence of myopia among primary and middle school students in Mengzi city is at a relatively high level. Female, Han nationality, higher learning stage, the time of doing homework daily in the past 1wk≥1h, occasionally or never keep your eyes more than one foot away from a book while reading and writing, insufficient sleep and parental myopia are all risk factors that can cause myopia.
RESUMO
La selección genómica (SG) es usada para predecir el mérito de un genotipo respecto a un carácter cuantitativo a partir de datos moleculares o genómicos. Estadísticamente, la SG requiere ajustar un modelo de regresión con múltiples variables predictoras asociadas a los estados de los marcadores moleculares (MM). El modelo se calibra en una población en la que hay datos fenotípicos y genómicos. La abundancia y la correlación de la información de los MM dificultan la estimación, y por ello existen distintas estrategias para el ajuste del modelo basadas en: mejor predictor lineal insesgado (BLUP), regresiones Bayesianas y aprendizaje automático. La correlación entre el fenotipo observado y el mérito genético predicho por el modelo ajustado, provee una medida de eficiencia (capacidad predictiva) de la SG. El objetivo de este trabajo fue realizar un meta-análisis de la eficiencia de la SG en cereales. Se realizó una revisión sistemática de estudios relacionados a SG y se llevó a cabo un meta-análisis, para obtener una medida global de la eficiencia de la SG en trigo y maíz, bajo diferentes escenarios (cantidad de MM y método estadístico usado para la SG). El metaanálisis indicó un coeficiente de correlación promedio de 0,61 entre los méritos genéticos predichos y los fenotipos observados. No se observaron diferencias significativas en la eficiencia de la SG realizada con modelos basados en BLUP (RR-BLUP y GBLUP), enfoque estadístico más comúnmente usado. El incremento de MM no cambia significativamente la eficiencia de la SG.
Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely.
RESUMO
The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.
Assuntos
Florestas , Esperança , Razão de Chances , Características da População , Viés de PublicaçãoRESUMO
The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.