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Stata package version 12

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STATA package version 12.0 is a comprehensive statistical software suite designed for data analysis, management, and visualization. It provides a wide range of tools and functions for various statistical techniques, including regression analysis, time series analysis, survey data analysis, and more. The package is widely used in academic and research settings.

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19 protocols using stata package version 12

1

Meta-Analysis of XPA A23G Polymorphism and Esophageal Cancer

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Statistical analysis was conducted using the Stata package version 12.0 (Stata Corporation, College Station, TX, USA). The χ 2 -test was used for determining the HWE of genotypes and the heterogeneity of rare allele frequencies in the control groups of each study reviewed. The association of XPA A23G polymorphism and esophageal cancer risk was estimated by odds ratios (ORs) with 95% confidence intervals (95% CIs). Depending on the results of the heterogeneity test among individual studies, the fixed-effect model (Mantel-Haenszel) or random-effect model (DerSimonian and Laird) was selected to summarize the pooled ORs and their 95% CIs.
The significance of the pooled OR was determined by a Z-test. Sensitivity analysis was evaluated by comparing the results of fixed-effects model and random-effects model. We also performed stratification analyses by geographic area(s) and source of controls. P <0.05 was considered to be statistically significant.
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2

Meta-Analysis of XRCC3 T241M Polymorphism and Colorectal Cancer

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We assessed HWE in the controls for each study using a chi-square test, and P < 0.05 was considered to indicate significant disequilibrium. The pooled odds ratio (OR) with corresponding 95% confidence interval (95%CI) was calculated to assess the strength of the association between the XRCC3 T241M polymorphism and CRC risk under homozygote comparison (TT vs MM), heterozygote comparison (TT vs MT), a dominant model (MM + MT vs TT), and a recessive model (TT + MT vs MM) between groups. Between-study heterogeneity was estimated using the I 2 test. I 2 ranges from 0-100% and represents the proportion of interstudy variability that can be attributed to heterogeneity rather than to chance. I 2 values of 25, 50, and 75% were defined as low, moderate, and high estimates, respectively. I 2 > 50% indicated heterogeneity across studies, and the random effects model was used for meta-analysis; otherwise, the fixed effects model was used. Subgroup analyses were performed by ethnicity and sample sizes. Sensitivity analysis was performed by removing the studies not in HWE. Funnel plot asymmetry was assessed by Begg's test to estimate potential publication bias (P < 0.05 indicated statistical significance). Meta-analysis was performed using the STATA package version 12.0 (Stata Corporation, College Station, TX, USA).
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3

Meta-analysis of ALDH2 Polymorphism and Alcohol-Related Cancer

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HWE was evaluated for each study using an internet-based HWE calculator (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). The risk of ALC associated with the ALDH2 polymorphism was estimated for each study by OR and 95% CI. The heterogeneity among the results was assessed by χ2-based Q test as well as the I2 statistic.26 (link) When a significant Q test (p>0.1) or I2<50% indicated homogeneity across studies, the fixed effects model was used,27 (link) or the random effects model was used.28 (link) Then, we performed stratification analyses on country. Analysis of sensitivity was performed to evaluate the stability of the results. The Begg's funnel plot and Egger's regression test were used to assess the publication bias.29 (link)30 (link) All statistical analyses were carried out using the Cochrane Collaboration RevMan 5.2 and STATA package version 12.0 (Stata Corporation, College Station, TX, USA).
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4

Meta-analysis of STAT3 Polymorphisms in IBD

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The meta-analysis was performed using the Cochrane Collaboration RevMan 5.1 and STATA package version 12.0 (Stata Corporation, College Station, TX, USA). The pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated to evaluate the association between the STAT3 rs744166 polymorphisms and UC and CD risk. In addition, subgroup analyses were performed based on ethnicity and study design when adequate data were available. A χ2-test based on the Q statistic was performed to assess the between-study heterogeneity. When I2>50% and P<0.1, the heterogeneity was considered to be significant, and the random effects model was used to analyze the data. The fixed effects model was chosen for homogeneous data. Egger's test was used to assess the publication bias. HWE was examined with the χ2 test. P<0.05 was considered to be significant. To adjust for multiple comparisons, the Bonferroni correction and false discovery rate (FDR) were applied. The power of the meta-analysis for each polymorphism to detect an effect size was estimated according to the method recommended by Hedges and Pigott [17] (link) with a significance value of 0.05.
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5

Efficacy and Safety of ESD vs EMR for Rectal Carcinoid

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The efficacy and safety of ESD therapy compared with EMR or m-EMR for the treatment of rectal carcinoid tumors was estimated for each study using the odds ratio (OR) and 95% confidence interval (95% CI). The mean differences (MD) with 95% CI were used for continuous variables (tumor size and procedure time). The χ2-test-based Q statistic test was performed to assess the between-study heterogeneity.21 (link) We also quantified the effect heterogeneity according to the I2 test. When a significant Q test (p<0.05) or I2 >50% indicated heterogeneity across studies, the random effects model was used.22 (link) Otherwise, the fixed effects model was applied.23 (link) An analysis of sensitivity was performed in order to evaluate the stability of the results. Finally, potential publication bias was investigated using Begg's funnel plot and Egger's regression test.24 (link),25 (link) A p value of <0.05 was regarded as being statistically significant.
All statistical analyses were performed using the Cochrane Collaboration RevMan 5.2 and STATA package version 12.0 (Stata Corporation, College Station, TX, USA).
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6

Statistical Analysis of COVID-19 Serum Sodium Levels

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We used SPSS for Windows, version 15.0. (SPSS Inc., Chicago, IL, USA) and STATA Package version 12.0 (StataCorp, College Station, TX, USA) for the exploratory data analysis and descriptive statistics, respectively. The Mann-Whitney U-test or t-test was performed for two groups according to the distribution or homogeneity of the continuous variables. Categorical variables are reported as frequencies and percentages. Nominal and categorical variables were compared using the chi-square test and Fisher's exact test, respectively. The association between serum sodium levels and outcomes was assessed by univariate analysis. Logistic regression analysis with stepwise backward elimination was performed to confirm the risk factors of hyponatremia at diagnosis in patients with COVID-19. The means of the parameters between the three CXR groups were compared using analysis of variance (ANOVA) for normally distributed variables and the Kruskal-Wallis test for non-normally distributed variables. Missing data were handled primarily by complete case analysis at the time of the statistical analysis. As a secondary analysis, missing data were imputed using multiple imputations. A p-value ≤ 0.05 was considered significant.
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7

Evaluating miR-181 Expression in AML

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The pooled HR with 95% CI was used to evaluate the correlation of miR-181 expression and the survival outcome of AML patients. The heterogeneity among all included studies was examined using Cochran’s Q test and Higgins I-square (I2) statistic. In case of no or moderate heterogeneity (P > 0.1 or I2 < 50%), the fixed-effect model (Mantel-Haenszel test) was applied; otherwise, the random-effects model (Der Simonian and Laird method) was used. Subgroup analysis and meta-regression were carried out to further explore possible explanations for heterogeneity [57 (link)]. In addition, Begg’s funnel plot and Egger’s bias were used to evaluate the potential publication bias [58 (link), 59 (link)]. If a publication bias did exist, the Duval and Tweedie nonparametric Trim and Fill method was used to adjust the results [60 (link)]. Sensitivity analysis was performed by removing one study at each time to assess its influence on the pooled HR. All analyses were conducted by STATA package version 12.0 (Stata Corporation, College Station, Texas, USA). A P value < 0.05 was considered to be statistically significant.
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8

Aspirin and Colorectal Cancer Risk

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In each study, incidence rates of CRC were calculated using the number of cases as the numerator and the respective person-time as the denominator, stratified by sex and age group. Nested case–control analyses were performed to estimate the association between low-dose aspirin and the occurrence of CRC. Under the study design of incidence density sampling, the odds ratio is an unbiased estimator of the incidence rate ratio (RR) [12 (link)]. Rate ratios and 95% confidence intervals (CIs) were calculated by unconditional multiple logistic regression models adjusted in each study for the frequency matching factors, number of PCP visits, smoking, non-steroidal anti-inflammatory drugs and BMI. Use of insulin and oral steroids were also included in the fully adjusted model in Studies 1 and 2, with adjustment for paracetamol monotherapy also undertaken in Study 2. All potential confounders were treated as categorical variables and a separate level was created for variables with missing information. Stratified analyses and tests for interaction were performed (S1 Text). Statistical analyses were carried out using STATA package version 12.0 (StataCorp LP, College Station, TX, USA).
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9

Survival Impact of miR-126 Expression

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HRs with their 95% CIs were combined to evaluated the effect of miR-126 expression on the survival outcome of cancer. Patients with overexpression of miR-126 indicated a better prognosis if HR < 1 and its 95% CI did not overlap with 1. Heterogeneity of pooled HRs was carried out using Cochran's Q-test and Higgins I-square (I2) statistic [30 (link), 31 (link)]. If there was significant heterogeneity (P < 0.05 or I2 > 50%.), the random-effects model (Der Simonian and Laird method) was used [32 (link)]. Otherwise, a fixed-effects model (Mantel-Haenszel test) was applied [33 (link)]. Subgroup analysis and metaregression were further performed to explore possible explanations for heterogeneity. Begg's funnel plot and Egger's bias were used to evaluate the potential publication bias [34 (link), 35 (link)]. Analysis of sensitivity was performed to evaluate the stability of the results. All statistical tests were two-sided, and P < 0.05 was regarded as statistically significant. All analyses were conducted using the Cochrane Collaboration RevMan 5.2 or STATA package version 12.0 (Stata Corporation, College Station, Texas, USA).
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10

Meta-analysis of Lymphocyte-to-Monocyte Ratio

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The aggregated HRs and 95% CIs were used to evaluate the impact of LMR on OS or DFS. The statistical variables were directly extracted from the study, or calculated from available numerical data based on the method suggested by Tierney et al.22 (link) An observed HR less than 1.0 implied a better prognosis in patients with high LMR. We performed the chi-square-based Q-statistic test and calculated the I-squared (I2) statistic to assess the interstudy heterogeneity. A fixed-effects model was selected if the heterogeneity among the enrolled studies was not significant (P>0.05 for the Q-test and I2<50%). Otherwise, a random-effects model was applied. Publishing bias of literatures was assessed by using the Begg’s funnel plot and Egger’s linear regression test. All P-values were two-tailed, and P<0.05 was considered to be statistically significant. Statistical analyses were conducted using Stata package version 12.0 (StataCorp LP, College Station, TX, USA).
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