Stata statistical program (version 14.2) was used for data analysis. Continuous variables were expressed as median and interquartile range (IQR) and were compared using the Kruskal–Wallis test. Categorical variables were expressed as frequencies and percentages and compared using the χ2 test or the Fisher’s exact test. All tests were two-tailed. Imputation of missing values was not performed. Oncologic outcomes were analyzed using the Kaplan–Meier method and the log-rank test. Univariate and multivariate adjusted logistic regression models were used for the comparison between groups. For the construction of the multivariate model, a selection method based on maximum likelihood estimation and Akaike information criterion (AIC) was used, considering all relevant variables related to the primary end point.
Stata statistical program version 14.2
Manufactured by StataCorp
Stata is a general-purpose statistical software package that provides a wide range of data analysis, data management, and graphics capabilities. Version 14.2 offers a comprehensive set of tools for researchers, analysts, and data scientists to explore, visualize, and model data. The software supports a variety of data types and formats, and provides a flexible programming language for customizing analyses and automating tasks.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using stata statistical program version 14.2
1
Comparative Oncologic Outcomes Analysis
2
Statistical Analysis of Oncologic Outcomes
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Stata statistical program (version 14.2) was used for data analysis. Continuous variables were expressed as median and interquartile range (IQR) and were compared using the Kruskal-Wallis test. Categorical variables were expressed as frequencies and percentages and compared using the χ 2 test or the Fisher's exact test. All tests were two-tailed.
Imputation of missing values was not performed. Oncologic outcomes were analyzed using the Kaplan-Meier method and the log-rank test. Univariate and multivariate adjusted logistic regression models were used for the comparison between groups. For the construction of the multivariate model, a selection method based on maximum likelihood estimation and Akaike information criterion (AIC) was used, considering all relevant variables related to the primary end point.
Imputation of missing values was not performed. Oncologic outcomes were analyzed using the Kaplan-Meier method and the log-rank test. Univariate and multivariate adjusted logistic regression models were used for the comparison between groups. For the construction of the multivariate model, a selection method based on maximum likelihood estimation and Akaike information criterion (AIC) was used, considering all relevant variables related to the primary end point.
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