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R version 4 2

Manufactured by StataCorp

R version 4.2.1 is a free, open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.

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2 protocols using r version 4 2

1

Meta-Analysis of Antibiotic Requests for URTI

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The percentage of parents who requested antibiotics from healthcare during the
consultation of their children for URTIs was calculated by dividing the number
of parents who wanted prescriptions by the total number of study participants.
The heterogeneity of the studies evaluated in this meta-analysis was assessed
using the I2 test. The heterogeneity was classified
as low, moderate, and high, respectively, based on
I2 values of less than 25%, 25%–50%, and more
than 50%. Cause for heterogeneity was also explored using meta-regression. A
random-effects model with 95% confidence intervals was used to evaluate the
overall effect and p-values of < 0.05 was considered
statistically significant. Subgroup analysis was done based upon geography
(continent) using a random intercept logistic regression model. R version 4..2.1
and STATA® software (version 16, STATA Corp.) were used to conduct
the meta-analysis.
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2

Age-Related Muscle Strength and Gait Assessment

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First, a description of the included variables was provided, estimating percentages for categorical variables and averages, as well as standard deviations for numerical variables. Subsequently, differences between men and women were assessed by applying a Pearson’s chi-square test for categorical variables and Student’s t-test for numerical variables. Finally, linear regression models were used to analyze the relationship between the dependent variables (i.e., ALSTI, handgrip strength, and gait speed) and each of the covariates while controlling for sex, mean energy intake (kcal/day), and the interaction between the exposure variable and sex. The estimated coefficients (Est), standard errors (SE), and p-values for each exposure variable were reported.
To measure the size of the effect of the studied associations, we utilized the Spearman’s Rho coefficient, assuming the following cutoff points: at least 0.8 (very strong), 0.6 up to 0.8 (moderately strong), 0.3–0.5 (fair), and <0.3 (poor) [20 ,21 ].
In addition, correlation graphs were constructed to visualize the association between the ALSTI and the statistically significant covariates. The statistical significance level was set at 0.05, and the Benjamini and Hochberg method was used to adjust for multiple comparisons [22 ]. The analyses were performed using R version 4.2.1 and STATA 17.0 software.
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