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Al desarrollar modelos de predicción para su aplicación en la práctica clínica, los profesionales de la salud suelen categorizar las variables clínicas que son de naturaleza continua. En muchas ocasiones estos modelos constituyen la base para la confección de escalas predictivas, a partir de las cuales se estratifica a los pacientes en varias categorías atendiendo al fenómeno estudiado. En estos casos se requiere la determinación de uno o varios puntos de cortes que permitan dividir el recorrido de la variable, variables continuas o puntuaciones de una escala, en dos o más categorías. El presente trabajo tiene como objetivo la automatización de diferentes métodos para dicotomizar variables continuas en modelos de predicción clínica, donde la variable respuesta es dicotómica, y determinar el punto de corte óptimo en la estratificación de pacientes en dos categorías, a partir de escalas de predicción. Para ello se elaboró un software en el lenguaje de programación R, que implementa diferentes métodos para la determinación del punto de corte óptimo, lo cual agiliza el trabajo investigativo de los especialistas de salud en el proceso de elaboración de modelos predictivos y/o escalas de predicción.
When developing predictive models for application in clinical practice, health professionals often categorize clinical variables that are continuous in nature. In many cases, these models are the basis for the development of predictive scales from which patients are stratified into various categories according to the phenomenon under study. In both cases, it is necessary to determine one or more cut-off points that allow dividing the path of the variable, continuous variables, or scores of a scale into two or more categories. The aim of the present work is to automate different existing methods for dichotomizing continuous variables in clinical prediction models where the response variable is dichotomous, as well as to determine the optimal cut-off point for stratifying patients into two categories, based on prediction scales. For this purpose, a software was developed in the R programming language, which implements different existing methods for the determination of the optimal cut-off point, speeding up the research work of health specialists in the process of developing predictive models and/or prediction scales.
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Objective:To evaluate the construction effect of famous doctor studio and the correlation among the acceptance indexes by analyzing the achievements of famous doctor studio in the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine.Methods:R software was used to count the relevant data of national famous medical studios approved from 2010 to 2018, and the advantages were analyzed by radar chart; Spearman correlation coefficient was calculated, and R software was used to draw the correlation heat map between the evaluation indexes; papers published by studio members during the construction period were retrieved, which were transformed into refworks format, imported into CiteSpace software to draw a visual knowledge map.Results:A total number of 506 articles were included in this study, involving 25 evaluation indexes and 22 national famous doctors' studios. The statistical analysis results showed that there was a significant positive correlation between the number of academic experience papers of famous and senior TCM experts published in core journals (V3) and the number of diagnosis and treatment schemes of dominant diseases (V1), and there was a significant negative correlation between 8 groups of indexes; Tinnitus, insomnia, premature ovarian failure, acupuncture and acupuncture therapy are research hotspots.Conclusions:When evaluating the construction effect of famous doctor studio, various indicators should be considered comprehensively, appropriately control of the number of members is helpful to improve the quality of talent training, and pay attention should be paid to experience exchange and thought collision during the training process.
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Background: Covid-19 pandemic forced people to get confined to social isolation and maintain social distancing which affected their mental health. The study aims at understanding the impact on the mental health status of people in Kerala, based on their demographic profile and the psychological issues faced during the Covid-19 lockdown. Methodology: The study included 700 respondents from various districts of Kerala telephonic and mailed questionnaires for the period of December 2020 - May 2021. Ordered logit model is used for the econometric analysis using R software. Results: The overall mental health status of an individual is affected by various reasons which are taken as various categories for the analysis. As a result of log odds estimated, Covid-19 patients are more disposed to mental health issues. Male respondents had a positive impact on mental health issues and unmarried people of all gender are disturbed with lockdown distress. Almost all occupational categories taken into account are more or less shaken with the pandemic except those with proper job security including Government and PSU workforces. Psychological, physical well-being is affected by the pandemic lockdown overall mental health system of people of Kerala. Conclusion: Covid-19 pandemic have affected the overall mental health of the people regardless of age and profession which can be more or less solved by the individuals themselves through yoga, meditation, exercise, reading books, gardening, cooking, virtual contacts with friends and family, getting enough sleep, etc. which helps tocan keep the mind relaxed and stable.
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The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the cohort design， the hypothesis test and interval estimation of relative risk， and the implementation of SAS and R software. In view of the characteristics of cohort design， the concept of relative risk， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical and professional conclusions.
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The purpose of this article was to introduce the χ2 test for the data of four-fold tables collected from the cross-sectional design and the realization of SAS and R software. Specifically， three methods were introduced， namely " Pearson΄s χ2 test"， "corrected Pearson΄s χ2 test" and "likelihood ratio χ2 test". The contents involved the test hypotheses， calculation principles and formulas， prerequisites， implementation of SAS and R software， interpretation of results and statement of conclusions of the aforementioned three statistical analysis methods.
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The purpose of this paper was to introduce the McNemar΄s χ2 test and SAS and R software implementation of four-fold table data collected from the matched pairs design. Firstly， it was proposed that there were three situations for the data of four-fold table of the paired design， namely ①the data of paired design four-fold table with the special "gold standard" was worthy of statistical analysis； ②the data of four-fold table of the paired design without the special "gold standard" was not worthy of statistical analysis； ③the data of four-fold table collected from the matched pairs design with implicit "gold standard" was worthy of statistical analysis. Secondly， taking the "problems and data" in the first case as the object of statistical analysis， SAS and R software were used to analyze the differences， the calculation results were given and explained， and the statistical and professional conclusions were also made.
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The purpose of this paper was to introduce the CMH χ2 test and SAS software implementation of the three kinds of R×C contingency table data. The first type was called “two-way unordered R×C contingency table data”. The CMH χ2 test corresponding to this type of data was essentially the Pearson’s χ2 test. The second type was called “R×C contingency table data with an ordinal outcome variable”. The CMH χ2 test corresponding to this kind of data was essentially a rank sum test. The third type was called “R×C contingency table data which was of two ordinal variables with different attributes”. The CMH χ2 test corresponding to the data was essentially Pearson’s correlation analysis or Spearman’s rank correlation analysis. When there were 1 or 2 “ordinal variables” in the R×C contingency table data， it was necessary to “assign or score” the ordinal variables before performing statistical analysis. In the FREQ procedure of SAS/STAT， there were four scoring methods. With different scoring approach， both the expression form and the calculation results of CMH χ2 test statistics could change accordingly.
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The purpose of this paper was to introduce the linear trend χ2 tests for the data of the two kinds of one-way ordinal two-dimensional contingency tables and the implementation of SAS and R software. The two one-way ordered two-dimensional contingency tables referred to the “R×2 contingency table” with R values ordinal variable as the cause variable and the “2×C contingency table”with C values ordinal variable as the outcome variable. Their linear trend tests required the use of the Cochran-Armitage’s linear trend χ2 test and the Lee’s linear trend χ2 test， respectively.
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The purpose of this article was to introduce the χ2 test and SAS and R software implementation of three kinds of two-way unordered two-dimensional contingency table data except the four-fold table data. The three kinds of the tables referred to the two-way unordered “R×2”“2×C” and “R×C” （both R and C were greater than 2） contingency tables. Generally speaking， the purpose of analyzing them was to test whether the two attribute variables in the contingency table were independent. When certain prerequisites were met， the simple statistical analysis method that could be used was the χ2 test， when the specific prerequisites were not met， the Fisher’s exact test， which required the large amount of calculation， should be selected.
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The purpose of this paper was to introduce the χ2 test for the data of four-fold tables collected from the case-control design， the hypothesis test and interval estimation of odds ratio， and the implementation of SAS and R software. In view of the characteristics of case-control design， the concept of odds ratio， hypothesis testing and interval estimation were emphasized. It was given that the interpretation of the results calculated by SAS and R software， as well as the statistical conclusion and professional.
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Objective To explore the application of time series autoregressive integrated moving average (ARIMA) based on seasonal difference to predict the number of syphilis cases in Anhui Province, and to provide a reference for early warning and control of syphilis. Methods Using R 3.6.2 software, the number of syphilis cases in Anhui Province from January 2004 to December 2016 was used for model fitting, and the resulting model was used to predict the incidence from January to December 2017. The difference between the predicted value and actual observed value was compared to evaluate the prediction effect of this model fitting. Results The incidence of syphilis in Anhui Province was on the rise with obvious periodicity. ARIMA(1,1,1)(0,1,2)12 was the optimal model, with the AIC being -264.81 and the BIC being -249.99. Box-Pierce test showed that λ2 value was 1.444(P=0.963), 10.459(P=0.576), and the difference was not statistically significant (P>0.05), indicating that the residual sequence was white noise. The model accuracy effect evaluation showed that the MAE was 0.06, the RMSE was 0.09, and the MAPE was 1.00%, indicating that the model fitting effect was good. The 2017 data was used to test the effect of the model extrapolation, and the results showed MAPE=6.09%, indicating that the model extrapolation effect was good. The actual value fell within 95% confidence interval of the predicted value, and the model prediction effect was relatively ideal. Conclusion The ARIMA(1,1,1)(0,1,2)12 model could better fit and predict the number of syphilis cases in Anhui Province, which may provide a theoretical basis for early warning, prevention and control of syphilis.
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Resumen Objetivo: analizar la incorporación laboral de los migrantes peruanos en la ciudad de Córdoba, Argentina, en el periodo 2001-2010. Métodos: Se realiza análisis estadístico descriptivo en la primera fase y análisis de correspondencias múltiples en la segunda fase (ACM), ambos en perspectiva comparada con la población nativa. Resultados: los hombres aumentan la participación en la industria, tanto los nativos como los migrantes. Sin embargo, en mayor magnitud para los hombres peruanos. Las mujeres nativas ven poco alterada su estructura ocupacional en el periodo. Para las mujeres nacidas en Perú el movimiento hacia el sector de la industria también se aprecia. Sin embargo, el rasgo más destacable para este grupo es la desconcentración de la categoría más frecuente: el trabajo doméstico, en un proceso que va en dirección a diversificar el espectro de ocupaciones. Conclusiones: en relación a los migrantes peruanos, el clivaje de género presenta estabilidad como un mecanismo que explica las inserciones diferenciales, donde las mujeres persisten en ocupaciones específicas en el periodo considerado. Esto apoya la hipótesis de una incorporación segmentada para las mujeres. Sin embargo, también se observa una variación, ya que aunque la categoría más frecuente sea el trabajo doméstico en ambos momentos censales, la menor proporción de ellas allí empleadas en el 2010, indica que otras ocupaciones adquieren relevancia. En el caso de los hombres el principal rasgo es el trasvase hacia la industria.
Abstract Objective: to analyze the labor incorporation of Peruvian migrants in the city of Córdoba, Argentina, in the period 2001-2010. Methods: We use descriptive statistical analysis in the first step and analysis of multiple correspondences in the second step (MCA), both in comparative perspective with the native population. Results: men increase participation in the industry, both natives and migrants. However, the magnitude is greater for Peruvian men. For native women, the change in the occupational structure is small. For women born in Peru the movement towards the industry sector is also appreciated. However, the most remarkable feature for this group is the scattering of the most frequent category: domestic work, in a process that goes in the direction of diversifying the spectrum of occupations. Conclusions: in relation to Peruvian migrants, gender cleavage presents stability as a mechanism that explains differential incorporation, where women persist in specific occupations in the period considered. This supports the hypothesis of a segmented incorporation for women. However, there is also a variation, since although the most frequent category is domestic work in both census periods, the lower proportion of them employed there in 2010 indicates that other occupations acquire relevance. In the case of men, the main feature is the transfer to industry.
Subject(s)
Humans , Male , Female , Argentina , Work , Human Migration , Occupations , PeruABSTRACT
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.
Subject(s)
Forests , Hope , Odds Ratio , Population Characteristics , Publication BiasABSTRACT
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.
Subject(s)
Forests , Hope , Odds Ratio , Population Characteristics , Publication BiasABSTRACT
Dados ambientais frequentemente apresentam valores censurados, perdidos e/ou discrepantes (outliers). Além disto, as amostras devem ser consideradas dependentes por terem componentes espaciais e temporais. Outro fato frequente nestes dados é que dificilmente seguem uma distribuição Normal ou Log-normal. Devido a estas características e outras, técnicas estatísticas convencionais não devem ser utilizadas. O presente trabalho apresenta um estudo de caso do rio das Velhas, Minas Gerais, utilizando métodos estatísticos robustos após o tratamento adequado dos dados. A análise de componentes principais detectou as variáveis que mais contribuem para a degradação da qualidade das águas do rio das Velhas e a visualização espacial dos escores mostrou onde esta contaminação está mais evidente.
Environmental data often presents censored, lost and/or outlier values. In addition, samples should be considered dependent for having spatial and temporal components. Another fact is that, frequently, these data won't follow a Normal or Log-normal distribution. Because of these and other characteristics, conventional statistical techniques should not be used. This article presents a case study of the Das Velhas river, Minas Gerais, using robust statistical methods after appropriate treatment of the data. The analysis of the main components found the variables that contribute the most for the degradation of water quality in the river, and the spatial visualization of the scores showed where this contamination is most evident.
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Objective:To explore the relationship between tobacco smoking and liver cancer risk.Methods:Cohort studies of cigarette smoking and risk of liver cancer were identified by searching MEDLINE and CBM databases from January 1989 to September 2009. Results from individual studies were combined synthetically by Meta analysis using R software and Meta package. Results:A total of 19 articles were included in our analysis. The cohort totally included 3 023 558 followed-up persons and 9 525 cases of liver cancer patients. The pooled relative risk (RR) of liver cancer was 1.49 (95%CI: 1.17-1.92) for smokers compared with non-smokers. The results showed a statistically moderate association between tobacco smoking and the risk of liver cancer. Subgroup analysis indicated that the pooled RRs for smoking were 1.53(95%CI : 1.29-1.83)for male smokers and 1.70(95%CI :1.17-2.48)for female smokers, respectively.Conclusion:Tobacco smoking is a risk factor for liver cancer in both males and females.
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Para disponibilizar um sistema de fornecimento de dados que objetivando-se subsidiar pesquisas de Melhoramento Genético Animal direcionadas à comparação de metodologias de avaliação genética, foi avaliado o comportamento da variância genética aditiva de populações selecionadas e não selecionadas, por seis gerações sucessivas, via simulação Monte Carlo. Por meio de um modelo genético aditivo, foram simuladas populações de 40 animais (20 machos e 20 fêmeas), sob seleção e acasalamento aleatório. Da geração zero até a quinta geração notou-se na população selecionada uma redução de 44,4 por cento na variância genética aditiva, devido a um aumento de 11,58 por cento no coeficiente de endogamia. Na população não selecionada a redução da variância genética aditiva foi menor (27,46 por cento) em relação à população selecionada, também devido a aumento de 10,26 por cento no coeficiente de endogamia.
The additive genetic variance in selected and unselected populations was evaluated in six successive generations via Monte Carlo simulation. The aim was to build a data system to help researches compare genetic evaluation methodologies in Animal Breeding. By means of an additive genetic model, populations of 40 individuals (20 males and 20 females) were simulated, under selected and random mating system. From the generation zero until the fifth generation, the selected population showed reduction of 44.4 percent in additive genetic variance due to an increase of 11.58 percent in inbreeding coefficient. In the unselected population the reduction in additive genetic variance was lower (27.46 percent) in relation to the selected population, due to the increasing of 10.26 percent in inbreeding coefficient.