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Stata 10.0 statistical software

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STATA 10.0 is a comprehensive statistical software package for data analysis, data management, and graphics. It provides a wide range of statistical techniques, including regression analysis, time series analysis, and survey data analysis. STATA 10.0 is designed to be user-friendly and offers a powerful programming language for automating complex tasks.

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Stata statistical software, by StataCorp (1 mentions)

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17 protocols using stata 10.0 statistical software

1

Continuous Variable Statistical Analysis

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Continuous variables were expressed as the mean ± standard deviation (SD). Statistical analysis was performed with the STATA10.0 statistical software package (STATA Corporation, College Station, TX, USA). The accuracy rate was examined with Fisher’s exact test. Findings with P < 0.05 were considered to be statistically different.
He Y., Ji X., Xie Y., He B., Xu X., Chen X, & Zhang Q. (2015). Clinical application of ultrasound-guided core needle biopsy with multiple punches in the diagnosis of lymphoma. World Journal of Surgical Oncology, 13, 126.
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2

Tumor Characteristics and Statistical Analyses

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Mean and SD for continuous variables, and relative frequency for categorical variables, were used as indices of centrality and dispersion of the distribution. For categorical variables, the Chi-square and z test for proportions were used; the Wilcoxon rank-sum (Mann-Whitney) test was to test the difference between two categories, and the Kruskal-Wallis rank test to test the difference among categories.
Linear regression model was used to evaluate the associations between maximum tumor diameter on single variables examined, while logistic regression model was used to evaluate the associations between PVT (No/Yes) or Nodule number (1/ ≥ 2), respectively on single variables examined.
All final multiple linear or logistic regression models was obtained with the backward stepwise method and the variables that showed associations with p<0.10 were left in the models.
When testing the null hypothesis of no association, the probability level of < error, two tailed, was 0.05.
All the statistical computations were made using STATA 10.0 Statistical Software (StataCorp). 2007. Stata Statistical Software: release 10. College Station, TX: StataCorp LP, Statistical Software (StataCorp). 2007. Stata Statistical Software: release 10. College Station, TX: StataCorp LP).
Carr B.I., Akkiz H., Üsküdar O., Yalçın K., Guerra V., Kuran S., Karaoğullarından Ü., Altıntaş E., Özakyol A., Tokmak S., Ballı T., Yücesoy M., Bahçeci H.İ., Ülkü A., Akçam T., Polat K.Y., Ekinci N., Şimşek H., Örmeci N., Sonsuz A., Demir M., Kılıç M., Uygun A., Demir A., Delik A., Arslan B., Doran F., Yilmaz S, & Tokat Y. (2018). HCC with low- and normal-serum alpha-fetoprotein levels. Clinical practice (London, England), 15(1), 453-464.
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3

Assessing Medication Adherence Factors

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Data was entered using double data entry, cleaned to ensure completeness and consistency and checked for normality. Univariate analysis was used to summarize data, describe social demographic characteristics of study respondents and determine level of adherence. We used bivariate analysis to determine associations between adherence and various independent factors using STATA 10.0 statistical software (Stata, College Station, TX, USA). Crude odds ratios with their corresponding 95% confidence intervals were used to describe these associations. Independent variables with p-values less than 0.25 were used for multinomial analysis to determine variables which were independently associated with medication adherence. Independent factors were determined through a logistic regression analysis with stepwise elimination method while keeping known predictors of adherence from literature, in the multivariate model.
Bagonza J., Rutebemberwa E, & Bazeyo W. (2015). Adherence to anti diabetic medication among patients with diabetes in eastern Uganda; a cross sectional study. BMC Health Services Research, 15, 168.
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4

Evaluating PD-L1 Expression in Cohorts

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Means and standard deviations (M ± SD) for continuous variables, and relative frequency (%) for categorical variables, were used as indices of centrality and dispersion of the distribution.
Chi-square test or Fisher exact test, depending on the number of observations, for categorical variables, Kruskal-Wallis rank test and Wilcoxon rank-sum test (Mann-Whitney test) for continuous variables were used to test differences among the groups. In the logistic regression models the absence of PD-L1 expression was considered as a reference category.
All models were adjusted for gender and age. Results were presented as Odds-Ratio (OR) and with 95% Confidence Intervals (C.I.). The Odds-Ratio represents the risk for one-unit variation of the predictor variable. When testing the hypothesis of significant association, p-value was < 0.05, two tailed for all analysis. All statistical computations used STATA 10.0 Statistical Software, (StataCorp), College Station, TX, USA.
Valentini A.M., Di Pinto F., Cariola F., Guerra V., Giannelli G., Caruso M.L, & Pirrelli M. (2018). PD-L1 expression in colorectal cancer defines three subsets of tumor immune microenvironments. Oncotarget, 9(9), 8584-8596.
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5

Statistical Analysis of Experimental Data

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The Stata 10.0 statistical software (Stata) was used for all statistical analysis. Results were expressed as means ± SEM. Differences among groups were subjected to a one-way analysis of variance (ANOVA) and P < 0.05 was considered significantly different.
Yang X.H., Pan Y., Zhan X.L., Zhang B.L., Guo L.L, & Jin H.M. (2016). Epigallocatechin-3-gallate Attenuates Renal Damage by Suppressing Oxidative Stress in Diabetic db/db Mice. Oxidative Medicine and Cellular Longevity, 2016, 2968462.
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6

Modeling Expired Time Constants in ARDS

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Descriptive statistics are displayed as mean ± standard deviation. Other values are displayed as mean and 95% confidence intervals (CI). Continuous variables were analyzed using paired t tests (within animal) or two‐sample t tests (between animals), where appropriate. All tests were two‐sided and the statistical significance was defined at P < 0.05. Statistical analyses were performed using STATA 10.0 Statistical Software (StataCorp, College Station, TX) and SAS (SAS Institute, Inc., NC).
Expired time constants values were analyzed by linear mixed‐effect model, including a random effect for each animal and fixed effects for segment, PEEP, VT, and pre–post injury.
Significance of model coefficient estimates, least squares means, and differences in least squares means were determined by T test. Main effects and interactions were confirmed by use of F tests with type III sums of squares. All tests were performed at the 0.05 significance level. Differences in least squares means were adjusted for multiple testing using the Tukey–Kramer adjustment.
Henderson W.R., Dominelli P.B., Molgat‐Seon Y., Lipson R., Griesdale D.E., Sekhon M., Ayas N, & Sheel A.W. (2016). Effect of tidal volume and positive end‐expiratory pressure on expiratory time constants in experimental lung injury. Physiological Reports, 4(5), e12737.
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7

Cancer Registry Data Verification

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Data were recorded using CanReg 5 software provided by the IARC (International Agency for Research on Cancer, Lyon, France).29 The verification was performed with necessary correction, including logic, range, and internal consistency, which were checked using Stata 10.0 Statistical Software (Stata Corp)28 and Epidata Software (The EpiData Association, Denmark).30
Kamsa-ard S., Luvira V., Suwanrungruang K., Kamsa-ard S., Luvira V., Santong C., Srisuk T., Pugkhem A., Bhudhisawasdi V, & Pairojkul C. (2019). Cholangiocarcinoma Trends, Incidence, and Relative Survival in Khon Kaen, Thailand From 1989 Through 2013: A Population-Based Cancer Registry Study. Journal of Epidemiology, 29(5), 197-204.
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8

Pulmonary Injury and Metabolic Profiling

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Values are displayed as mean ± standard deviation (SD). Physiological variables and pulmonary mechanical data were analyzed using a three‐way ANOVA and Tukey's correction for multiple comparisons analysis. Injury score analysis was performed using a two‐way ANOVA and Tukey's correction for multiple comparisons analysis. Statistical significance was defined at p < 0.05. Statistical analyses were performed using GraphPad Software (Version 8.2.1, La Jolla, CA, USA). Metabolomic analysis was performed using a two‐way ANOVA where appropriate. Statistical analyses were performed using STATA 10.0 Statistical Software (StataCorp, College Station, TX) and SAS (SAS Institute, Inc., NC). Metabolomics statistical analysis was described above.
Angus S.A., Henderson W.R., Banoei M.M., Molgat‐Seon Y., Peters C.M., Parmar H.R., Griesdale D.E., Sekhon M., Sheel A.W., Winston B.W, & Dominelli P.B. (2022). Therapeutic hypothermia attenuates physiologic, histologic, and metabolomic markers of injury in a porcine model of acute respiratory distress syndrome. Physiological Reports, 10(9), e15286.
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9

Analysis of Tumor Diameter Associations

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Mean and SD for continuous variables, and relative frequency for categorical variables, were used as indices of centrality and dispersion of the distribution. For categorical variables, the Chi-square and z test for proportions were used.
The Pearson’s correlation was used to measure the association between two continuous variables and the Wilcoxon rank-sum (Mann-Whitney) test, to test the difference between two categories, and the Kruskal-Wallis rank test to test the difference among categories. Linear regression model was used to evaluate the associations between maximum tumor diameter on single variables examined. The final multiple linear regression were obtained with the backward stepwise method and the variables that showed associations with p<0.10 were left in the models.
When testing the null hypothesis of no association, the probability level of α error, two tailed, was 0.05. All the statistical computations were made using STATA 10.0 Statistical Software (StataCorp. 2007. Stata Statistical Software: release 10. College Station, TX: StataCorp LP, Statistical Software (StataCorp. 2007. Stata Statistical Software: release 10. College Station, TX: StataCorp LP,
Akkiz H., Carr B.I., Kendal Y.K., Guerra V., Kuran S., Altıntaş E., Üsküdar O., Karaoğullarından Ü., Özakyol A., Tokmak S., Yücesoy M., Bahçeci H.İ., Ülkü A., Akçam T., Polat K.Y., Ekinci N., Şimşek H., Örmeci N., Sonsuz A., Demir M., Kılıç M., Uygun A., Ballı T., Demir A., Arslan B, & Doran F. (2017). Characteristics of hepatocellular carcinoma aggressiveness factors in Turkish patients. Oncology, 94(2), 116-124.
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10

Statistical Analysis of Tumor Characteristics

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Means and standard deviations (M±SD) for continuous variables, and relative frequency for categorical variables, were used as indices of centrality and dispersion of the distribution. Chi-square test for categorical variables, Kruskal-Wallis rank test and Wilcoxon rank-sum test (Mann-Whitney test) for continuous variables was used to test associations between groups. Z test for proportions was used for comparison between two categorical variables.
A multiple logistic regression model was used to evaluate association between either PVT or tumor nodule multifocality and selected parameters. When testing the hypothesis of significant association, p-value was <0.05, two tailed for all analyses. All statistical computations used STATA 10.0 Statistical Software, (StataCorp), College Station, TX, USA.
Carr B.I., Guerra V., Giannini E.G., Farinati F., Ciccarese F., Rapaccini G.L., Marco M.D., Benvegnù L., Zoli M., Borzio F., Caturelli E., Chiaramonte M, & Trevisani F. (2014). Association of abnormal plasma bilirubin with aggressive HCC phenotype. Seminars in oncology, 41(2), 252-258.
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