Correlations between 2 groups were determined with the Fisher exact test and Mann‐Whitney U test. Survival distributions were estimated with the Kaplan‐Meier method, and groups were compared with the log‐rank test. Univariate Cox regression analyses were performed to assess the effects of prognostic factors. Only variables that were statistically significant in the univariate analysis were subsequently evaluated in the multivariate analysis. The multivariate analysis was performed with a forward/backward stepwise method, and P < .05 was the threshold for inclusion in the model. All statistical analyses were performed with the STATA software package, version 15 (Stata Corporation, College Station, TX, USA).
Stata software package version 15
Manufactured by StataCorp
Sourced in United States
STATA software package version 15.1 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of statistical tools and methods for various fields of study. The software package includes features for data manipulation, regression analysis, time-series analysis, and more.
Automatically generated - may contain errors
Lab products found in correlation
5 protocols using stata software package version 15
1
Multivariate Survival Analysis Techniques
2
Analyzing Randomized Controlled Trial Data
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Data analysis and reporting were performed according to the CONSORT guidelines for randomized controlled trials. The analysis was conducted according to the intention-to-treat principle. Analyses were performed using the STATA software package version 15 (StataCorp, College Station, TX, USA, 2017). The proportions between the groups were compared using the Chi-Square test or Fisher’s exact test if appropriate. The continuous data were compared using Student’s t-test or the Mann–Whitney U test if the distribution was skewed. Kruskal–Wallis tests were used to compare the means between several groups. The associations were considered statistically significant when the p-value was below 0.05 (probability of type I error = 0.05). We evaluated the intensity score of the QDSA based on the sum of the intensity scores provided by the women. Data were stored in a relational database management system using the dedicated clinical database management system software REDCapTM, a mature, secure web application for building and managing online surveys and databases.
3
Comparison of 16S rDNA and gltA Genes
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Univariable analysis was performed using McNemar’s Chi-squared test for prevalence comparison between the 16S rDNA and gltA genes. The statistical analysis was performed using the STATA software package version 15.1 (Stata Corporation, College Station, TX, USA). Results were considered significantly different for p < 0.05.
4
Evaluation of Liver Injury in COVID-19
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Categorical variables are reported as counts and percentages, while continuous variables are reported as medians (interquartile range [IQR]). Differences in baseline characteristics, key outcomes, and laboratory parameters between the liver injury cohort and control patients were evaluated. We performed a subgroup analysis on patients with severe COVID‐19 to determine if similar findings were applied within the disease severity class. Chi‐square test and Kruskal‐Wallis test were used to describe differences in categorical and continuous variables, respectively. P < 0.05 was determined as statistically significant. Spearman’s rank correlation analysis was performed to identify relationships between elevated liver enzymes and key inflammatory markers. Statistical analyses were performed using the STATA software package (version 15.1; STATA, College Station, TX).
5
Statistical Analysis of Demographic Data
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Statistical analyses were performed with the Stata software package, version 15.1
(StataCorp, College Station, TX, USA). Values of p < 0.05
were considered statistically significant.
Demographic data are expressed as mean and standard deviation. Categorical
variables are expressed as proportion and 95% confidence interval (95% CI).
Comparisons between categorical variables were made by using the chi-square
test. The Shapiro-Wilk test was used in order to assess the distribution of
continuous variables. To compare continuous variables between groups, we used
the Student’s t-test if the distribution was normal and the Mann-Whitney U-test
if it was nonparametric.
The level of interobserver agreement was calculated with the method described by
Cohen(25 ). The respective values for its interpretation were
as follows: kappa (κ) of 0.0-0.20 = weak agreement; κ of 0.21-0.40
= fair agreement; κ of 0.41-0.60 = moderate agreement; κ of
0.61-0.80 = excellent agreement; and κ of 0.81-1.0 = almost perfect
agreement.
(StataCorp, College Station, TX, USA). Values of p < 0.05
were considered statistically significant.
Demographic data are expressed as mean and standard deviation. Categorical
variables are expressed as proportion and 95% confidence interval (95% CI).
Comparisons between categorical variables were made by using the chi-square
test. The Shapiro-Wilk test was used in order to assess the distribution of
continuous variables. To compare continuous variables between groups, we used
the Student’s t-test if the distribution was normal and the Mann-Whitney U-test
if it was nonparametric.
The level of interobserver agreement was calculated with the method described by
Cohen(25 ). The respective values for its interpretation were
as follows: kappa (κ) of 0.0-0.20 = weak agreement; κ of 0.21-0.40
= fair agreement; κ of 0.41-0.60 = moderate agreement; κ of
0.61-0.80 = excellent agreement; and κ of 0.81-1.0 = almost perfect
agreement.
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