Stata version 15

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Stata version 15 is a data analysis and statistical software package. It provides a range of tools for data management, statistical analysis, and visualization. Stata version 15 supports a variety of data types and offers a comprehensive set of statistical procedures.

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Lab products found in correlation

Sas version 9, by SAS Institute (65 mentions) Stata statistical software release 15, by StataCorp (28 mentions) R version 3, by R Project for Statistical Computing (19 mentions) Spss version 25, by IBM (18 mentions) Stata statistical software, by StataCorp (13 mentions) Prism 8, by GraphPad (10 mentions) Sas 9, by SAS Institute (9 mentions) Sas version 9, by StataCorp (9 mentions) Spss version 20, by IBM (8 mentions) R version 4, by R Project for Statistical Computing (8 mentions) Spss version 21, by IBM (5 mentions) Spss version 26, by IBM (5 mentions) Stata, by StataCorp (5 mentions) Spss version 25, by StataCorp (5 mentions) Prism 9, by GraphPad (5 mentions) Spss version 22, by IBM (5 mentions) Stata 15, by StataCorp (4 mentions) Spss version 23, by IBM (4 mentions) Epidata version 3, by StataCorp (4 mentions) Spss statistics version 26, by IBM (3 mentions)

2 896 protocols using stata version 15

Surface Roughness Comparison of Debonded Surfaces

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Normality assumptions were checked through Shapiro–Wilk tests and q–q plots. Due to the non-normal distribution of the residuals, nonparametric statistics were used. Descriptive statistics were used to present actual reference and debonded values as well as differences (Δ = debonded-reference) for roughness parameters. The following parameters were explored: Sa, Sz, Sc, Sv, and Sdr. A Wilcoxon signed-rank test for paired data was used to check similarity between debonded and initial roughness values for each parameter. The level of statistical significance was pre-specified at p < 0.05. Statistical analyses were performed with STATA version 15.1 software (Stata Corporation, College Station, Tex, USA).
All analyses were undertaken in STATA version 15.1 software (StataCorp, College Station, Texas, USA).

Eichenberger M., Iliadi A., Koletsi D., Eliades G., Verna C, & Eliades T. (2019). Enamel Surface Roughness after Lingual Bracket Debonding: An In Vitro Study. Materials, 12(24), 4196.

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Sensitivity Analysis for Dichotomous and Continuous Outcomes

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Sensitivity analyses were performed using Review Manager version 5.3 to calculate the pooled OR with a 95% CI for dichotomous variables, and the MD with a 95% CI for continuous variables, which were reexamined by Stata version 15.0. Heterogeneity among included studies was measured using a Q test and I² statistic. When the p value of Q test >0.1 and I² < 50% indicated no evident heterogeneity, a fixed-effects model was used; otherwise, a random-effects model was carried out. A p value <0.05 was considered a statistically significant difference. Publication bias was evaluated with a funnel plot via Review Manager version 5.3, and Begg’s and Egger’s tests via Stata version 15.0.

Jiang B., Zhou L., Lu J., Wang Y, & Guo J. (2020). Predictors of lymph node metastasis and residual tumor in early gastric cancer patients after noncurative endoscopic resection: a systematic review and meta-analysis. Therapeutic Advances in Gastroenterology, 13, 1756284820935033.

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Glycopyrrolate and Maternal Hemodynamics

Cited 1 time

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Normality of data was checked using histogram, Kurtosis Skewness test, and Shapiro-Wilk test. For continuous variables with normal and non-normal distribution, mean (SD) and median (interquartile range) were used respectively. For categorical variables, the percentage of frequency was used. Student t-test and Mann–Whitney U-test were applied for continuous data which showed normal and non-normal distribution respectively. The categorical data was compared using the chi-square test. Fisher exact test was used instead, when the expected values in any of the cells of a contingency table were < 5. A P value of less than 0.05 was considered statistically significant. All analyses were conducted using STATA version 15.0 (Stata Corporation, College Station, TX, USA).
Sample size calculation was based on the mean amount of phenylephrine required for maintaining maternal hemodynamics during elective cesarean section under spinal anesthesia which was 501 (154) μg in parturients receiving glycopyrrolate as compared to 552 (118) μg in those who did not receive glycopyrrolate [9 (link)]. To detect this difference, we needed 114 subjects in each group with a power of 80%, at a two-sided alpha level of 0.05. Allowing for a 15% dropout rate during the study period, a total of 258 patients were enrolled (STATA version 15.0, Stata Corporation, College Station, TX, USA).

Deshar R., Subedi A., Pokharel K., Sah B.P, & Prasad J.N. (2022). Effect of glycopyrrolate on vasopressor requirements for non-elective cesarean section under spinal anesthesia: a randomized, double-blind, placebo-controlled trial. BMC Anesthesiology, 22, 327.

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Comparing Statistical Methods for Time Series

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Analyses were performed using Stata version 15 [35 ]. A range of visual displays were constructed to compare the performance of the statistical methods. Frequency distributions were plotted to visualise the level- and slope-change estimates, autocorrelation coefficient estimates, and the results of the Durbin-Watson test for autocorrelation. Scatter plots were used to display the mean values for empirical and model-based SEs, coverage, power and autocorrelation coefficient estimates. Line plots were used to show confidence intervals for the level and slope change estimates. Results and summaries of the analyses were summarised (using the simsum package [34 (link)]) and graphed using Stata version 15 [35 ].

Turner S.L., Forbes A.B., Karahalios A., Taljaard M, & McKenzie J.E. (2021). Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Medical Research Methodology, 21, 181.

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Meta-analysis of Dichotomous Outcomes

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The dichotomous outcomes were evaluated using OR and 95% CI. The data from the Kaplan-Meier (KM) curve was extracted using the Engauge Digitizer V.4.1 software, and the HR and 95 % CI were calculated [23] (link). The I 2 was used to determine the degree of study heterogeneity. When I 2 >50%, indicating considerable heterogeneity between trials, a random-effects model was utilized. A xed-effects model was utilized in the other cases. For data processing and statistical analysis, the Nordic Cochrane Center, Cochrane Collaboration, Copenhagen, Denmark, and Stata version 15.0 software (Stata Corporation, College Station, TX, United States) were used. P <0.05 was deemed statistically signi cant.

Liu B., Lu T., Wang Y., Chen Y., Ma S., Jiao Y., Yang K., & Cai H. (2021). Clinicopathological significance and prognosis of long noncoding RNA SNHG14 expression in human cancers: A Meta-Analysis and bioinformatics analysis.

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Meta-analysis of MTHFR Polymorphisms and PD

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The Hardy-Weinberg equilibrium (HWE) was calculated by the Chi-squared test in control groups in all of the research works; the P-value more than 0.001 demonstrated that the population was in genetic equilibrium. Besides that, the odds ratios (ORs) that had 95 percent confidence interval (CI) were adopted for the purpose of calculating the strength of the link between PD susceptibility and the MTHFR polymorphisms. The importance associated with the accumulated OR was investigated in accordance with the Z-test; in addition, P<0.05 was regarded as having statistical significance. The evaluation of the between-study heterogeneity was carried out with the help of the Q-test as well as I² statistics [14 (link)]. Moreover, the application of the fixed-effects framework was made at P_h>0.1 or I²<50% [15 (link)]; or else, the random-effects framework was followed [16 (link)]. The sensitivity analysis was carried out through the omission of the single research work always for the evaluation of the robustness of the findings. Also, the underlying publication partiality was assessed with the help of the Begg’s funnel plots and Egger’s test [17 (link), 18 (link)]. All of the statistical analysis was carried out with the use of the STATA version 15.0 software (Stata Corporation, College Station, TX, USA). A P-value below 0.05 was regarded as having statistical significance.

Diao H.M., Song Z.F, & Xu H.D. (2019). Association Between MTHFR Genetic Polymorphism and Parkinson’s Disease Susceptibility: A Meta-analysis. Open Medicine, 14, 613-624.

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COVID-19 Mortality Risk Factors

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Patients’ basic descriptive data and clinical presentation, complications, and admission were summarized as frequencies and percentages. Continuous data of the descriptive statistics were presented as mean and standard deviation. Pearson Chi-square test was used to analyze categorical variables and Student’s t test for continuous variables. Univariate analysis (Table 2) was performed against our primary outcome (death), and variables significant at p < 0.05 were selected for inclusion in our multivariate logistic regression models. Two multivariate logistic regression models were carried out; one with age, BMI, and comorbidities (Model 1, Table 3); the other with age/BMI score and comorbidities, excluding age and BMI (Model 2, Table 3). Results were presented as odds ratio (OR) and 95% confidence interval (CI). Statistical analysis was performed with STATA version 15.0 software (StataCorp, College Station, TX).

Al Balwi W., Al Turki M., Memish Z.A., Fakhoury H.M., Al Balwi M, & Hajeer A.H. (2022). Age/BMI is a Stronger Predictor of Death in COVID-19 Patients than Age Alone: A Pilot Study. Journal of Epidemiology and Global Health, 12(4), 548-551.

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Pneumococcal Serotyping and Antibiotic Resistance

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For logistic reasons, it was not possible to serotype all the pneumococcal isolates obtained during the 3 years of the study. A random selection of approximately half the isolates (698/1,418) (49.2%) was selected for serotyping.
Baseline sociodemographic and clinical data were collected on hard copy case report forms, and laboratory results were recorded in the first instance in laboratory books before being transferred into an electronic database using Excel. Data were cleaned and analyzed with Stata version 15.0 software (Stata Corp LLC, College station, TX). Data were presented as proportions and compared using chi² or Fisher’s exact test. A Poisson regression model was used to estimate the prevalence ratios between treatment arms at each survey. Vaccine serotypes (0 or 1) and resistance to azithromycin (0 or 1) were the dependent variables, whereas the treatment arm (0 or 1) and the survey (1–6) were the independent ones. The significance threshold used for statistical tests was P < 0.05.

Hema-Ouangraoua S., Zongo I., Kabore N.F., Frédéric N., Yerbanga R.S., Tinto H., Compaore Y.D., Kuepfer I., Chandramohan D., Greenwood B, & Ouedraogo J.B. (2020). Serotype Profile of Nasopharyngeal Isolates of Streptococcus pneumoniae Obtained from Children in Burkina Faso before and after Mass Administration of Azithromycin. The American Journal of Tropical Medicine and Hygiene, 103(2), 679-683.

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Prevalence and Antibiotic Resistance of Mycoplasma genitalium

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A descriptive analysis was performed to assess the MG prevalence per visit in each country. We also scrutinized the occurrence of MG per anatomical site of infection and per symptomatology. In case an individual was positive for MG at >1 anatomical site, the infection was only accounted as 1. STI symptoms were categorized as having urogenital, anorectal, or oropharyngeal complaints. Statistical associations of sociodemographic and behavioral characteristics with detection of MG at least once were assessed using logistic regression models. Characteristics associated with outcome with a P value < .2 in univariate analyses were entered in the complete multivariate model. A manual backward selection was used to determine the final multivariate model. To calculate the prevalence of antibiotic resistance, we used samples of the first and of a new MG episode (negative in between visits). All analyses were performed using Stata version 15.0 software (StataCorp, College Station, Texas).

De Baetselier I., Vuylsteke B., Cuylaerts V., Cuer B., Yaya I., Mensah E., Dah E., Anoma C., Koné A., Diandé S., Dagnra A., Fayé-Ketté H., Yeo A., Smet H., Van den Bossche D., Keita B.D., Spire B., Laurent C, & Crucitti T. (2022). Mycoplasma genitalium and Antimicrobial Resistance Among a Cohort of West African Men Who Have Sex With Men Using Preexposure Prophylaxis (CohMSM-PrEP ANRS 12369-Expertise France Study). Open Forum Infectious Diseases, 9(11), ofac615.

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Propensity Score Matching Analysis of Survival Outcomes

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Programming and statistical analyses were performed using STATA version 15.0 software (Stata Corporation, College Station, TX, USA) and R version 4.0.2 (Stanford University, CA, USA). Continuous variables were expressed as the mean ± standard deviation, and between‐group comparisons were performed using the Student's t‐test. Categorical data were presented as frequencies, and between‐group differences were evaluated using Pearson's chi‐squared test. The propensity score model included variables such as age, gender, alpha‐fetoprotein (AFP) level, hepatitis B virus (HBV) infection, ECOG score, Child‐Pugh class, main tumor size, vascular invasion (VI), and extrahepatic spread (EHS). A 1:1 matched analysis using nearest‐neighbor matching with a caliper width of 0.3 without replacement was performed to deduce matched pairs from two groups based on the estimated propensity score. Survival data, including OS and TTP, were estimated by the Kaplan–Meier method and the comparison was performed using log‐rank test. Univariate and multivariate analyses were conducted using the log‐rank test and Cox regression analysis for variables, with clinical significance and p < 0.1 in univariate analyses. All statistical tests were two‐sided, and p < 0.05 were considered significant.

Fan W., Zhu B., Yue S., Zheng X., Zou X., Li F., Qiao L., Wu Y., Xue M., Wang H., Tang Y, & Li J. (2022). Idarubicin‐Loaded DEB‐TACE plus Lenvatinib versus Lenvatinib for patients with advanced hepatocellular carcinoma: A propensity score‐matching analysis. Cancer Medicine, 12(1), 61-72.

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