Stata se statistical software version 12

Manufactured by StataCorp

Sourced in United States

Stata/SE is a comprehensive statistical software package designed for data analysis, management, and visualization. Version 12.0 offers a wide range of statistical tools and functions to support advanced data processing and research applications.

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Lab products found in correlation

Statview software, by Abacus (3 mentions) Spss predictive analytics software version 18, by IBM (1 mentions)

8 protocols using stata se statistical software version 12

Systematic Review of TNF-α Inhibitors

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Using WMD, standardized mean difference (SMD) and RR, we performed a systematic review and meta-analysis of the aforementioned five endpoints according to the follow-up time and the type of control drugs used. For global perceived effect (satisfaction) or return to work (combined endpoint), an RR>1 indicated that the outcomes of the TNF-α inhibitor group were superior to those of the control group; for discectomy or radicular block (combined endpoint), an RR<1 indicated that the outcomes of the TNF-α inhibitor group were superior to those of the control group; for Oswestry Disability Index, VAS-leg, and VAS-lower back, a negative WMD or SMD indicated that the outcomes of the TNF-α inhibitor group were superior to those of the control group. The data from reports concerning same trial were used for the analysis of the corresponding follow-up. Prior to the meta-analysis, for each endpoint, Cochran’s Q statistic test was applied to assess the heterogeneity among the included studies. If a p-value of Cochran’s Q statistic (Qp) ≥0.10, which indicated the absence of heterogeneity, a fixed-effects model was applied; otherwise, a random effects model was applied for analysis. Stata statistical software version SE 12.0 (Stata Corp LP, College Station, TX, USA) was utilized for all statistical analysis.

Wang Y.F., Chen P.Y., Chang W., Zhu F.Q., Xu L.L., Wang S.L., Chang L.Y., Luo J, & Liu G.J. (2014). Clinical Significance of Tumor Necrosis Factor-α Inhibitors in the Treatment of Sciatica: A Systematic Review and Meta-Analysis. PLoS ONE, 9(7), e103147.

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Meta-Analysis Protocol for Endpoint Comparison

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Based on the formula and endpoint definition, the values of the same endpoints in each trial were pooled first and then the data from different trials were pooled together for analysis. The standardized mean difference (SMD) and risk ratio (RR) were used to assess the abovementioned endpoints. Prior to the meta-analysis of each endpoint, statistical heterogeneity across the various trials was tested using Chi-square test. A P-value greater than the nominal level of 0.10 and I² ≤40% indicated a lack of heterogeneity across trials, allowing for the use of a fixed-effects model; otherwise, a random-effects model was used. The inverse variance method was used for continuous variables, and the Mantel–Haenszel method was used for dichotomous variables. In addition, a sensitivity analysis was conducted by removing each trial one at a time, and the publication bias was evaluated using the Egger test.
SPSS Predictive Analytics Software version 18.0 (SPSS, Inc., Chicago, IL, USA) was used for the Chi-square tests, and Stata Statistical Software version SE 12.0 (Stata Corp. LP, College Station, TX, USA) was used for all other analyses.

Liu G.J., Karim M.R., Xu L.L., Wang S.L., Yang C., Ding L, & Wang Y.F. (2017). Efficacy and Tolerability of Gabapentin in Adults with Sleep Disturbance in Medical Illness: A Systematic Review and Meta-analysis. Frontiers in Neurology, 8, 316.

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Statistical Analyses of Experimental Data

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Statistical analyses were carried out using one-way ANOVA with Bonferroni’s post-hoc test (Stata/SE statistical software version 12.0; StataCorp; College Station, TX) or Mann-Whitney test (Statview software, Abacus Concepts; Piscataway, NJ). All data are shown as mean +/− SD. In all tests, p-values less than 0.05 were considered statistically significant.

Gonzalez-Andrades M., Alonso-Pastor L., Mauris J., Cruzat A., Dohlman C.H, & Argüeso P. (2016). Establishment of a novel in vitro model of stratified epithelial wound healing with barrier function. Scientific Reports, 6, 19395.

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Factors Associated with Cigarette Smoking

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Chi-square (χ²) tests were used to assess the statistical significance of relationships between current cigarette smoking status and individual-level, dyadic-level, and family-level variables. Unadjusted and adjusted logistic regression analyses were used to calculate odds ratios (ORs), adjusted odds ratios (aORs), and corresponding 95% confidence intervals (CI). Variable selection for the adjusted model was based on a combination of evidence from prior literature, a priori theory, and χ² p-values of <0.05. Variables selected for the adjusted model included: sex, age, marital status, income, past 30 day alcohol use, any past 30 day drug use, depressive symptoms, past 6 month participation in a 12-step program, family smoking, and main Supporter smoking. All analyses were performed using STATA SE statistical software version 12.0 [35 ].

Pacek L.R., Latkin C., Crum R.M., Stuart E.A, & Knowlton A.R. (2014). Current cigarette smoking among HIV-positive current and former drug users: associations with individual and social characteristics. AIDS and behavior, 18(7), 1368-1377.

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Assessing Smoking Risk Perceptions

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T-tests assessed differences in perceived risk based on instruction set type. Adjusted linear regression analyses calculated adjusted beta (β) coefficients and 95% confidence intervals (CI) to describe associations between sample characteristics and perceived health risks of smoking-related disease based on current smoking. Covariates in the adjusted model were selected based on a combination of p<0.05 in bivariate models and a priori theory, and included: age, use of ARVs, interest in quitting, provider recommendation to quit, and frequency of cessation-related discussions. Multicollinearity between variables in adjusted models were assessed: no evidence thereof was identified. Analyses were conducted using STATA SE statistical software version 12.0 (StataCorp, 2011 ).

Pacek L.R., McClernon F.J., Rass O., Sweizter M.M, & Johnson M.W. (2018). Perceived risk of developing smoking-related disease among persons living with HIV. AIDS care, 30(10), 1329-1334.

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Mortality Calibration and Statistical Analysis

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Data were evaluated using the Stata/SE statistical software version 12.0
(StataCorp, College Station, TX, USA) and Microsoft Office Excel 2013.
Calibration was performed by correlation between observed and expected mortality
through the Hosmer-Lemeshow test. Quantitative variables were described as means
and standard deviation and their differences were verified using the
Kruskal-Wallis test. Qualitative variables were expressed as absolute frequency
and proportions. Association between categorical variables and outcomes were
verified using Fisher's exact tests. Results were considered statistically
significant when P value was < 0.05.

Fortes J.V., Silva M.G., Baldez T.E., Costa M.D., da Silva L.N., Pinheiro R.S., Fecks Z.S, & Borges D.L. (2016). Mortality Risk After Cardiac Surgery: Application of Inscor in a University Hospital in Brazil's Northeast. Brazilian Journal of Cardiovascular Surgery, 31(5), 396-399.

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Longitudinal Analysis of Indoor and Outdoor PM2.5 on Airway Black Carbon

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Baseline descriptive statistics were analyzed using Spearman correlations, χ² tests, and t-tests, as appropriate. At baseline, linear regression models were created to assess the effect of indoor and outdoor PM_2.5 on AM black carbon content. To investigate the longitudinal relationship between indoor and outdoor PM_2.5 and AM black carbon, generalized estimating equations (GEE) were used to account for the potential correlation of repeated measures over time (Diggle et al., 2002 ). A covariate analysis was conducted to assess potential confounding variables, including age, sex, race, education, BMI, time since smoking cessation, pack-years, and season of sampling. Covariates of pack-years of smoking and BMI were associated in either cross-sectional or longitudinal analyses with airway black carbon content in bivariate analyses with a p < 0.10 and were subsequently included in multivariate models. Indoor and outdoor PM_2.5 models were run independently and simultaneously to determine the independent effects of indoor PM_2.5. Analyses were performed with StataSE statistical software, version 12.0 (Stata Corp, College Station, TX). Statistical significance was defined as p < 0.05.

Belli A.J., Bose S., Aggarwal N., DaSilva C., Thapa S., Grammer L., Paulin L.M, & Hansel N.N. (2016). Indoor particulate matter exposure is associated with increased black carbon content in airway macrophages of former smokers with COPD. Environmental research, 150, 398-402.

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Plasma IL-22 Impacts Metabolic Disorders

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Continuous data were presented as the mean ± standard deviation, or median with interquartile range (IQR), where indicated. Differences between groups were evaluated using the unpaired Student's t‐test for normally distributed data, and the Mann–Whitney test for data with skewed distributions. Multiple testing was corrected using the Bonferroni correction. The trends in categorical data were compared using the trend test. Multiple logistic regression analysis was carried out to assess the odds ratio (OR) for the incidence of the IFG and type 2 diabetes mellitus according to quartile groups of plasma IL‐22 level (in comparison with quartile 4). Four statistical models were used. The preliminary model was crude and considered plasma IL‐22 level only, whereas model 1 was adjusted for age, sex and BMI, and model 2 was adjusted for model 1 plus systolic blood pressure (SBP), diastolic blood pressure (DBP), TG, total cholesterol, HDL‐C and low‐density lipoprotein cholesterol. Model 3 was adjusted for the variables in models 2 plus alanine aminotransferase, aspartate transaminase and glutamyltranspeptidase. All data were analyzed with the Stata/SE statistical software version 12.0 (Stata Corporation, College Station, Texas, USA). P‐values were two‐sided, and <0.05 was considered to show statistically significant differences.

Shen J., Fang Y., Zhu H, & Ge W. (2017). Plasma interleukin‐22 levels are associated with prediabetes and type 2 diabetes in the Han Chinese population. Journal of Diabetes Investigation, 9(1), 33-38.

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