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Stata version 14.2 statistical software

Manufactured by StataCorp
Sourced in United States

Stata version 14.2 is a statistical software package developed by StataCorp. It is designed to perform a wide range of statistical analyses, data management, and visualization tasks. The software provides a comprehensive set of tools for regression analysis, time-series analysis, survey data analysis, and more.

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5 protocols using stata version 14.2 statistical software

1

Infant Body Composition at 2 Weeks

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Variables measured in the interval scale are summarized as means and standard deviations, whereas variables recorded in the ordinal or nominal scale are summarized as counts and percentages. The chi-square test was used to test the frequencies of variables in the ordinal or nominal scales. Maternal associations with offspring %FM were first evaluated using the entire cohort (n=209). Then, a secondary analysis was conducted in the subset of participants (n=136) for whom maternal-father-offspring data were available.
The association between infant’s %FM at two weeks of age and parental/infant characteristic was assessed using linear regression, whereas multiple linear regression was used to model infant’s %FM at two weeks of age and all characteristics simultaneously. The most parsimonious linear regression model was constructed using least absolute shrinkage and selection operator (LASSO) linear regression, although the same model resulted using step-wise linear regression. Data management and analysis was performed using the Stata version 14.2 statistical software (Stata Corp., College Station Texas).
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2

Meta-Analysis of Metabolic Syndrome Prevalence

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STATA version 14.2 Statistical software (StataCorp, College Station, Texas, USA) was used for the analysis, and heterogeneity was checked across studies by computing the I2 statistical test. We assumed no, low, medium, and high heterogeneity across the studies if the I2 values were 0%, 25%, 50%, and 75%, respectively. A random effects model was used to analyze the pooled estimated prevalence with 95% confidence intervals (CI) using the “metaprop” command, since significant heterogeneity was detected between studies. Funnel plots for visual inspection and Egger’s and Begg’s rank tests were used to assess the evidence of publication bias. A forest plot was used to report the estimated pooled prevalence of MetS and its subcomponents.
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3

Maternal Obesity and Offspring Body Composition

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Variables measured in the interval scale are summarized as means and standard deviations, whereas variables recorded in the ordinal or nominal scale are summarized as counts and percentages. The association between offspring’s %FM during the first two years of life and each maternal/infant characteristic was assessed using regressions, whereas mixed models were used to model offspring %FM during the first two years of life and all characteristics simultaneously using a time-varying covariate approach when measures were available at each trimester (e.g. REE, cytokines). The most parsimonious model was constructed using least absolute shrinkage and selection operator (LASSO) linear regression. In light of the sexual dimorphism observed in animal models and clinical studies in response to maternal obesity (6 (link),19 (link)) models were further build independently for male (N=128) and female (N=96). Data management and analysis was performed using the Stata version 14.2 statistical software (Stata Corp., College Station Texas) and R version 3.6.1, whereas the recursive partitioning was performed using JMP Pro version 13.0 statistical software (SAS Institute Inc., Cary, NC).
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4

Longitudinal Lung Function Analysis

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Univariate analysis was carried out using descriptive statistics to explore the characteristics of the study populations. Paired t tests and Wilcoxon sign rank tests (with bootstrapping to estimate the CI of the difference) were used to assess change between the two time points. Time was used as a continuous variable to account for variation between follow-up dates and to calculate an annualized change. A linear regression model was used to estimate the effect of potential factors (change in inhaled illicit drug use, change in tobacco smoking, and change in inhaler use) on changes in FEV1 over time. Variables were selected for the model a priori based on clinical data which might have varied over the course of follow-up within an individual, specifically in participants who described changes in drug or medication use. The whole model is presented without variable elimination. Data were analyzed using Stata version 14.2 statistical software (StataCorp LLC) and R version 3.4 (R Foundation for Statistical Computing). Statistical significance was tested at the conventional 5% level.
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5

Statistical Analysis with Bootstrap

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Bootstrap methods with 1000 replications were applied to estimate confidence intervals (CIs) for the correlation parameters. P values <0.05 were considered statistically significant and all P values were two-sided. Data were analyzed using STATA version 14.2 statistical software (StataCorp, College Station, TX, USA).
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