Stata statistical software package version 12

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STATA statistical software package version 12.0 is a comprehensive, integrated software package for data analysis, data management, and graphics. It provides a wide range of statistical and data management capabilities, including regression analysis, time-series analysis, and data visualization.

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19 protocols using stata statistical software package version 12

Meta-analysis of Hyperemesis Gravidarum and Helicobacter Pylori

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We used pooled odds ratio (OR) with its corresponding 95% confidence interval (CI) to estimate the strength of the association between HG and H. pylori infection. Post hoc subgroup analyses were also performed to explain the heterogeneity in results. Subgroups were explored as follows: detection of H. pylori infection (serum H. pylori IgG/IgM/IgA antibody by ELISA, stool antigen test, mucosal biopsy from endoscopy, or H. pylori genome by PCR), publication period (1996–2000, 2001–2005, 2006–2010, or 2011–2014), and region (Asia, North America, Europe, Africa, or Oceania).
The heterogeneity of the studies included in this meta-analysis was assessed using the Q statistic test and the I² statistic test. The random-effects model was selected when P value < 0.1 or I² > 50%; otherwise, the fixed-effects model was selected. Possible publication bias was evaluated by visual inspection of funnel plots and application of Begg's and Egger's test [11 (link)–15 (link)]. P values of less than 0.05 from Egger's test were considered statistically significant.
All statistical analyses were done with STATA statistical software package version 12.0 (2000; STATA Corp., College Station, TX, USA); P < 0.05 was identified as statistically significant.

Li L., Li L., Zhou X., Xiao S., Gu H, & Zhang G. (2015). Helicobacter pylori Infection Is Associated with an Increased Risk of Hyperemesis Gravidarum: A Meta-Analysis. Gastroenterology Research and Practice, 2015, 278905.

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Meta-analysis of Observational Studies

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The collected information from each identified study include: the first author, year of publication, country, average age, OR, and 95 % CI, and adjusted factors. Two reviewers extracted the data from each study independently and finally verified the extracted data. The meta-analysis was performed using the Stata statistical software package, version 12.0 (StataCorp, College Station, Texas, USA). The random effect model was employed during all the analyses. The heterogeneity was assessed through the I² statistic.

Shen L, & Ji H.F. (2015). Vitamin D deficiency is associated with increased risk of Alzheimer’s disease and dementia: evidence from meta-analysis. Nutrition Journal, 14, 76.

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Meta-Analysis of Survival Outcomes

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Calculation for dichotomous variables was carried out using the risk ratio (RR) and their 95% confidence interval (CI) as the summary statistic. Interstudy heterogeneity among the included studies was evaluated by the I² statistics.[6 (link)] Time-to-event data including the 3-year OS, 3-year progression-free survival (PFS), 5-year OS, and the 5-year PFS were extracted from individual trials. Pooled categorical comparisons were made by Chi-squared test. If the I² was larger than 50%, implying significant statistical heterogeneity between studies, the random effects (DerSimonian-Laird method) model was adopted; in the presence of no observable interstudy heterogeneity (I² < 50%), the fixed-effect model was applied. Two-sided P < 0.05 was considered statistically significant. Sensitivity analysis was performed to evaluate the stability of the results. Each study involved in the meta-analysis was removed each time to reflect the influence of the individual data set on the pooled effects. Evidence of publication bias was evaluated using the Begg's test[7 (link)] and Egger's test.[8 (link)] All analyses were performed using STATA statistical software package version 12.0 (STATA Corp., College Station, Texas, USA).

Han Y., Yan D., Xu F., Li X, & Cai J.Q. (2016). Radiofrequency Ablation versus Liver Resection for Colorectal Cancer Liver Metastasis: An Updated Systematic Review and Meta-analysis. Chinese Medical Journal, 129(24), 2983-2990.

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Smoking Prevalence and Cotinine Analysis

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Prevalence/percentage figures with 95% confidence intervals (95% CI) were calculated and chi-squared tests for associations between smoking and cohort, sex and residential region (ABC only) were done using a p-value less than 0.05 for statistical significance. Urinary cotinine was treated as a continuous measurement and all other variables were treated as categorical. Comparisons of cotinine levels between groups were done using the Wilcoxon rank-sum test. All statistical analyses were done using the Stata statistical software package, version 12.0 (StataCorp, College Station, TX).

Pearce M.S., Mann K.D., Singh G., Davison B, & Sayers S.M. (2014). Prevalence and validity of self-reported smoking in Indigenous and non-Indigenous young adults in the Australian Northern Territory. BMC Public Health, 14, 861.

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Predictive Role of CRP in STS

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Statistical analysis was carried out using the STATA statistical software package version 12.0 (Stata Corporation, College Station, TX). Combined HRs and Forrest plots were used to estimate the predictive role of CRP levels in STS patients. The Cochrane Q test (P<0.05 indicated a high level of heterogeneity) and I² (values of 25%, 50% and 75% corresponding to low, moderate, and high degrees of heterogeneity, respectively) was used to evaluate the heterogeneity between eligible studies. When homogeneity was good, a fixed-effect model was used; When heterogeneity was high, a random-effect model was used [24 (link)]. An observed HR > 1 indicated worse outcome for higher CRP levels. Begg’s test and Egger’s test on asymmetry of funnel plot were performed to test any existing publication bias. If evidence of publication bias was found, trim and fill method was adopted to check and revise the combined HRs [25 (link)]. Meta-regression analyses and subgroup analyses were performed to investigate the sources of heterogeneity [26 (link)]. Sensitivity analysis was also conducted to assess the influence of each individual study on the strength and stability of the combined HRs. All statistical tests were two-tailed and p < 0.05 was considered statistically significant.

Wang X., Liu S., Zhao X., Fang E, & Zhao X. (2019). The value of C-reactive protein as an independent prognostic indicator for disease-specific survival in patients with soft tissue sarcoma: A meta-analysis. PLoS ONE, 14(7), e0219215.

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Prognostic Value of SPARC in Pancreatic Cancer

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The primary outcome was OS associated with SPARC expression in patients with pancreatic cancer. HR and 95% CI were used to be the effect measure of interest. A combined HR>1, with its 95% CI did not overlap 1, indicated a worse survival for the group with high SPARC expression. The heterogeneity among studies was measured using the Q and I² test. A random or Fixed model was used according the heterogeneity analysis. A random effect model was applied if I²≧50%; the fixed effect model was selected if I²<50%. When I²≧50%, subgroup analyses would be carried out. A P < 0.05 indicates a significant factor contributing to the observed heterogeneity. The latent publication bias was assessed by a funnel plot and Egger’s linear regression test, and a value <0.05 indicated an inevitable significant publication bias[19 (link)]. All statistical tests were two-tailed and P<0.05 was considered statistically significant. All the analyses were conducted by Review Manager software version 5.3 (The Cochrane Collaboration) and STATA statistical software package version 12.0 (Stata Corporation, College Station, TX).

Han W., Cao F., Chen M.B., Lu R.Z., Wang H.B., Yu M., Shi C.T, & Ding H.Z. (2016). Prognostic Value of SPARC in Patients with Pancreatic Cancer: A Systematic Review and Meta-Analysis. PLoS ONE, 11(1), e0145803.

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Diagnostic Test Evaluation Protocol

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Sensitivity and specificity of diagnostic tests were calculated according to the following formulas: SE = TP/(TP + FN)*100, SP = TN/(TN + FP)*100; PPV, NPV and accuracy was calculated as follows: PPV = TP/P, NPV = TN/N, Accuracy = TP/(TP + TN)*100. FN: False negative; FP: False positive; N: Negative; NPV: Negative predict value; P: Positive; PPV: Positive predict value; SE: Sensitivity; SP: Specificity; TN: True negative; TP: True positive.
All of presented dHMGS dye intensity traces show a representative outcome form an individual run from gastric biopsy specimen selected from total dataset subjected to analysis. Data were statistically analyzed by χ2 test using Stata statistical software package version 12.0 (StataCorp College Station, TX, USA). Additionally, Gwet’s AC1 values were calculated to measure the detection agreement of dHMGS assay with sequencing using AgreeStat version 2011.3 (Advanced Analytics, MD, USA) [8 (link)]. The benchmark scales for Gwet’s AC1 values was based on the Landis and Koch’s criteria [9 (link)]. The H. pylori distribution of different groups was analyzed by Mann–Whitney rank-sum test for two variables and Kruskal–Wallis H test for more than two variables. All of the above hypothesis tests were two-sided; and a two-tailed p-value of 0.05 or less was considered to indicate statistical significance.

Zhang Y., Wang S., Hu B., Zhao F., Xiang P., Ji D., Chen F., liu X., Yang F., Wu Y., Kong M., Nan L., Miao Y., Jiang W., Fang Y., Zhang J., Bao Z., Olszewski M.A, & Zhao H. (2016). Direct detection of Helicobacter pylori in biopsy specimens using a high-throughput multiple genetic detection system. Future microbiology, 11, 1521-1534.

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Meta-analysis of Tenascin-C Expression

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Meta-analysis was carried out using STATA statistical software package version 12.0 (STATA Corp, College Station, Texas). Pooled HRs and corresponding 95% CIs were calculated to evaluate the impact of TN-C expression on OS. The χ²-based Q test and I² test were used to evaluate the heterogeneity across the studies. Random effect model was used for meta-analysis if there was significant heterogeneity (P ≤ .05, I² ≥ 50%), otherwise, fixed effect model was used. Besides, subgroup analysis was performed to explore the sources of heterogeneity. Meanwhile, we used funnel plots and Begg’s bias test to analyze the publication bias in this study.^{20 (link)}

Ming X., Qiu S., Liu X., Li S., Wang Y., Zhu M., Li N., Luo P., Liang C, & Tu J. (2019). Prognostic Role of Tenascin-C for Cancer Outcome: A Meta-Analysis. Technology in Cancer Research & Treatment, 18, 1533033818821106.

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Meta-analysis of Prognostic Biomarker PLR in Esophageal Cancer

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HR and 95%CI were obtained directly from each literature or estimation according to the methods by Parmer et al [40 (link)]. A HR>1 indicated a worse prognosis in esophageal cancer patients with high expression of PLR. For each meta-analysis, Cochran’s Q test and Higgins I-squared statistic were undertaken to assess the heterogeneity of the included trials. I²>50% is considered as a measure of severe heterogeneity. Both fixed-effects (Mantel–Haenszel method) and random effects (DerSimonian–Laird method) models were used to calculate the pooled HRs and 95%CIs. The random-effects model was used if there was heterogeneity between literatures; otherwise, the fixed-effects model was adopted.
Subgroup analysis was conducted to explore and explain the diversity (heterogeneity) among the results of different studies. Publication bias was assessed by Begg’s funnel plot and Egger’s bias test [41 (link)]. All P values were two-tailed, A P<0.05 was considered statistical significant. Statistical analyses were performed using STATA statistical software package version 12.0 (STATA, College Station, TX, USA).

Zhao Q.T., Zhang X.P., Zhang H, & Duan G.C. (2017). Prognostic role of platelet to lymphocyte ratio in esophageal cancer: A meta-analysis. Oncotarget, 8(67), 112085-112093.

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Berberine Inhibits Tumor Growth

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We carried out statistical analysis by using the Review Manager software (RevMan 5.3) and STATA statistical software package version 12.0 (Stata Corporation, College Station, TX). The primary outcomes were tumor volume, tumor weight, and tumor vessel density of BBR group compared with the control group. The secondary outcome was the change of body weight. Mean value and standard difference (SD) were used as summary statistics. Standard mean difference (SMD) was measured for continuous data. Linear regression and Pearson’s correlation analysis were used to study the The dose–response relationship between BBR and the four outcomes. The heterogeneity among studies was measured by using the I² test. The latent publication bias was assessed by using a funnel plot. All statistical tests were two-tailed, and p < 0.05 was considered statistically significant.

Xu J., Long Y., Ni L., Yuan X., Yu N., Wu R., Tao J, & Zhang Y. (2019). Anticancer effect of berberine based on experimental animal models of various cancers: a systematic review and meta-analysis. BMC Cancer, 19, 589.

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