Stata 12.0 statistical software

Manufactured by StataCorp

Sourced in United States

Stata 12.0 is a general-purpose statistical software package developed by StataCorp. It provides a comprehensive set of tools for data management, analysis, and visualization. Stata 12.0 supports a wide range of statistical methods, including regression analysis, time series analysis, and multilevel modeling, among others. The software is designed to be user-friendly and offers a command-line interface as well as a graphical user interface.

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Revman 5, by StataCorp (1 mentions)

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40 protocols using stata 12.0 statistical software

Meta-analysis of Immune Checkpoint Inhibitors in Head and Neck Cancer

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This meta-analysis was performed in accordance with recommendations from the Cochrane Collaboration and the Quality of Reporting of Meta-analyses guidelines [44 (link),45 (link)]. The HR was used as a summary statistic for censored outcomes (OS, DFS, PFS, DSS and DMFS). HRs > 1 represented a poor prognosis in HNC.
Heterogeneity among the primary studies was evaluated by Cochrane’s Q statistic and the I² statistic. A P value < 0.10 in Cochrane’s Q test or an I² value > 50% indicates substantial heterogeneity among studies, so a random effects model was used to calculate the pooled HR and 95% CI in such cases. Otherwise, a fixed effects model was applied.
We used the mean sample size as the boundary between studies with large and small sample sizes. Subgroup analyses were carried out according to the immune checkpoint molecule, ethnicity, sample size and tumor location. Sensitivity analysis was applied to high-quality studies (NOS ≥ 7). Begg’s funnel plots were used to assess publication bias. All statistical analyses were conducted with STATA 12.0 statistical software (Stata Corporation, College Station, TX, USA). A two-tailed P value < 0.05 was considered statistically significant.

Jia Y.Q., Yang B., Wen L.L., Mu W.X., Wang Z, & Cheng B. (2019). Prognostic value of immune checkpoint molecules in head and neck cancer: a meta-analysis. Aging (Albany NY), 11(2), 501-522.

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TGF-β1 Polymorphisms and GVHD Risk

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The associations between TGF-β1 polymorphisms and GVHD risk was assessed using odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) in donors and recipients, respectively. Dominant model was used in this meta-analysis, because most of the studies reported the results in this model. The heterogeneity of the included trials was assessed by the Cochrane's Q statistic for each meta-analysis. We carried out both fixed-effects (Mantel–Haenszel method) and random effects (DerSimonian–Laird method) models and producted the pooled HRs. In addition, subgroup analyses were performed to investigate the potential causes of heterogeneity according to ethnicity and HLA status. Publication bias was evaluated if more than ten studies were included. All analyses were performed by using stata 12.0 statistical software (Stata Corporation, College Station, TX, USA). All P values were two-sided.

Zhang L., Mao L, & Xu J. (2015). Transforming growth factor-β1 polymorphisms and graft-versus-host disease risk: a meta-analysis. Oncotarget, 7(3), 2455-2461.

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Diagnostic Yield of EBUS-TBNA for Sarcoidosis

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All statistical analyses were performed using Review Manager 5.2 statistical software and Stata 12.0 statistical software (Stata Corporation, College Station, TX, USA). We extracted the dichotomous data from the data presented in each primary study for each sarcoidosis diagnosis. We compared the diagnostic yields of the EBUS-TBNA and standard bronchoscopic modalities by calculating the odds ratios (ORs) and 95% CIs for each study and then pooling the data using the random-effect or fixed-effect model to calculate a pooled efficacy and CI. We assessed the influence of statistical heterogeneity on the pooled estimates of the individual results using the I² test. An I² value of ≥50% indicated significant heterogeneity. A value of P < 0.05 was considered significant for the Chi-square test of heterogeneity. We performed sensitivity analysis in which a subgroup analyses of the retrospective studies versus nonretrospective studies (including RCT and prospective studies). Sensitivity analysis was also conducted in which the studies were grouped based on whether rapid on-site evaluation (ROSE) had been performed. The presence of publication bias was evaluated by generation of a funnel plot, in which the OR was plotted. We also assessed publication bias via Begg's test and Egger's linear regression test using Stata 12.0.

Hu L.X., Chen R.X., Huang H., Shao C., Wang P., Liu Y.Z, & Xu Z.J. (2016). Endobronchial Ultrasound-guided Transbronchial Needle Aspiration versus Standard Bronchoscopic Modalities for Diagnosis of Sarcoidosis: A Meta-analysis. Chinese Medical Journal, 129(13), 1607-1615.

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Glioma Risk and Mobile Phone Use

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The association between mobile phone use and glioma risk was demonstrated by OR and its 95% confidence interval (95% CI). The statistical heterogeneity among the studies was evaluated by Chi-square test. The publication was assessed by funnel plot and line regression test. Stata12.0 statistical software was used for all the statistical analysis (http://www.stata.com). Two-tail P < 0.05 was deemed as statistical significance.

Wang Y, & Guo X. (2016). Meta-analysis of association between mobile phone use and glioma risk. Journal of cancer research and therapeutics, 12(Supplement).

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Demographic Factors and Endoscopic Findings

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All analyses were performed using Stata 12.0 statistical software (StataCorp LP, College Station, TX, USA). Descriptive statistics were calculated for demographic features (sex and age). After generating descriptive counts and proportions for symptom variables, Chi-square tests were used to compare patients with different demographic features or symptom variables for diagnostic yield. Then, univariable and multivariable logistic analyses were performed to assess the relationship between demographic features or symptom variables and the presence of a positive endoscopic or histologic abnormality. Significance was set at p < 0.05.

Wang S., Qiu X., Chen J., Mei H., Yan H., You J, & Huang Y. (2022). Pediatric esophagogastroduodenoscopy in china: indications, diagnostic yield, and factors associated with findings. BMC Pediatrics, 22, 522.

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Meta-analysis of Oncology Trials

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Pooled data management and analysis were performed using STATA 12.0 statistical software. We used the fixed-effects models (Mantele-Haenszel method) when there was a lack of heterogeneity (P >0.1 and I² <50%), and otherwise, we used the random-effects model (DerSimoniane-Laird method). The heterogeneity evaluation and the inconsistency statistic (I²) is explained as follows: I² = 0% indicates no heterogeneity, 0%

Wang C., Li Y., Ke L., Cao L., Fan P., Wu Z, & Wu Q. (2019). The Impact of Afatinib on Survival in Advanced Non-Small Cell Lung Cancer: A Meta-Analysis of Randomized Controlled Trials. Journal of Cancer, 10(4), 885-892.

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Evaluating SHOX2 Methylation as Lung Cancer Biomarker

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All data was managed by STATA12.0 statistical software (Stata Corp LP). The diagnostic sensitivity = true positive/(true positive + false negative), specificity = true negative/(true negative + false positive). The summary receiver operating characteristic (SROC) cure was applied to evaluate the feasibility of SHOX2 promoter methylation as biomarker for lung diagnosis. Fagan's nomogram was used to investigate the post‐test diagnostic probability. Deek's funnel plot and line regression test were used to evaluate the publication bias. Two‐tailed p < 0.05 was considered statistically significant.

Zhou X., Lu X., Wu H., Liu J, & Huang H. (2021). Diagnostic performance of SHOX2 promoter methylation as biomarker for lung cancer identification: A meta‐analysis update. Thoracic Cancer, 12(24), 3327-3332.

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Sample Size Calculation for Diarrhea Intervention

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We based sample size calculations for both the baseline and endline surveys on ORS coverage because zinc coverage was very low prior to the MI–led project in Bihar. To calculate the sample size required at baseline, we assumed baseline ORS coverage of 20.9% based on a previously published survey among children with diarrhea in the last 14 days [9 ]. In order to calculate the sample size required at endline, we assumed ORS coverage would increase from the 19.7% observed in the baseline survey to at least 28.5%. We increased the endline sample size to 750 caregivers of a child 2–59 months of age (ie, 50%) to ensure a sample big enough to demonstrate what is still an increase of public health importance. The sample size calculations for both surveys were conducted using STATA 12.0 statistical software (College Station, Texas, USA), with standard statistical assumptions (ie, two–sided test; alpha = 5%; 80% power; and non–continuity) and were increased to account for within village clustering and a 15% anticipated refusal rate [10 ].

Walker C.L., Taneja S., Lamberti L.M., Black R.E, & Mazumder S. (2015). Public sector scale–up of zinc and ORS improves coverage in selected districts in Bihar, India. Journal of Global Health, 5(2), 020408.

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Prognostic Significance of VISTA Expression

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The association between the expression of VISTA and patients’ prognosis was evaluated by meta-analysis by collecting data from all included studies. We calculated outcome endpoints including OS, DSS and TSS via pooled HRs and 95% CIs. HRs > 1 indicated a poor prognosis. The correlation between the expression of VISTA and the clinicopathological characteristics was evaluated by pooled RRs and 95% CIs. Cochrane’s Q statistic and the I² statistic were used to assess the heterogeneity among the included studies. A random effects model was used to calculate pooled HRs and 95% CIs when there was substantial heterogeneity (Q test: P < 0.1 or an I² > 50%). If not, a fixed effects model was used. Studies of high quality(NOS scores ≥ 7) were selected for sensitivity analysis. RevMan 5.3 and Stata 12.0 statistical software (Stata Corporation, College Station, TX, USA) were used to perform all statistical analyses. A difference was considered significant with a two-tailed p < 0.05.

He X.L., Zhou Y., Lu H.Z., Li Q.X, & Wang Z. (2020). Prognostic value of VISTA in solid tumours: a systematic review and meta-analysis. Scientific Reports, 10, 2662.

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Survival Analysis of Patient Cohort

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Descriptive statistics were used to characterise the patient population. Variables were presented as absolute numbers and percentage or median values and range. The chi-square test was used to analyse the significance of difference in the proportion of variables, and Kaplan-Meier analysis was used to assess the five-year survival. Statistical analysis was performed by SPSS 19.0 software, and the meta-analysis was conducted using Stata 12.0 statistical software. Heterogeneity was assessed with I-squared statistics. An I-squared value of more than 50% was considered to indicate high statistical heterogeneity.

Xu D, & Hou L. (2019). Clinicopathologic characteristics of mixed epithelial/mesenchymal metaplastic breast carcinoma (carcinosarcoma): a meta-analysis of Chinese patients. Polish journal of pathology : official journal of the Polish Society of Pathologists, 70(3).

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