Stata statistical package

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STATA is a general-purpose statistical software package. It provides a wide range of data manipulation, visualization, and statistical analysis tools for academic and professional researchers. STATA allows users to import, manage, and analyze data, as well as generate publication-quality graphs and reports.

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Stata statistical software release 15, by StataCorp (1 mentions) Review manager software, by StataCorp (1 mentions) Stata statistical software release 10, by StataCorp (1 mentions) Simca p software, by Sartorius (1 mentions) Spss v18, by IBM (1 mentions) Spss version 26, by IBM (1 mentions) Stata statistical software release 16, by StataCorp (1 mentions) Prism 6, by GraphPad (1 mentions)

95 protocols using stata statistical package

Leptin's Impact on Offspring Weight

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Quantitative variables were summarized as median and Interquartile range (IQR, 25th−75th percentiles). Qualitative variables were described as counts and percentage. The Kruskal–Wallis and Fisher exact tests were applied to compare quantitative and qualitative variables between the three groups of neonates (FT, PT and IUGR). For the analysis, we used a zero-skewness log-transformation of leptin concentrations. Generalized linear regression models, with Sandwich standard errors (to allow for intragroup correlation), were fitted to assess the association of the log-transformed leptin and the increase of offspring weight, while adjusting for neonate type and time. Using residuals derived from the model, the partial correlation of leptin and weight, was computed together with its 95% bootstrapped confidence interval (95% CI), overall and by neonate type. The corresponding scatterplots are presented. The interaction of leptin concentration and groups was tested to assess whether any effect modification by group was present.
A 2-sided p-value < 0.05 was considered statistically significant. The Bonferroni correction was used for post hoc comparisons. Data analysis was performed with the STATA statistical package (StataCorp. 2019. Stata Statistical Software: Release 17. StataCorp LLC: College Station, TX, USA).

Garofoli F., Mazzucchelli I., Angelini M., Klersy C., Ferretti V.V., Gardella B., Carletti G.V., Spinillo A., Tzialla C, & Ghirardello S. (2022). Leptin Levels of the Perinatal Period Shape Offspring’s Weight Trajectories through the First Year of Age. Nutrients, 14(7), 1451.

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Statistical Analysis of Patient Outcomes

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For descriptive statistics, we used absolute and relative frequencies for categorical variables and mean and standard deviation for continuous variables. Associations between categorical outcomes and other variables were assessed using Fisher’s exact tests in unadjusted analysis and logistic regression in analysis adjusted for confounders. Patient groups were compared in terms of continuous outcomes using Student’s t tests (if distributional assumptions were satisfied) or Wilcoxon’s rank sum tests (otherwise). The significance criterion was set at the conventional p < 0.05. Data handling and analysis were performed using version 15 of the Stata statistical package (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC).

Herman T., Csehely S., Orosz M., Bhattoa H.P., Deli T., Torok P., Lagana A.S., Chiantera V, & Jakab A. (2022). Impact of Endocrine Disorders on IVF Outcomes: Results from a Large, Single-Centre, Prospective Study. Reproductive Sciences, 30(6), 1878-1890.

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Comparison of Qualitative and Quantitative Data Analysis

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Qualitative variables were described as counts and percentages. Quantitative variables were expressed as the mean value and standard deviation (SD) when normally distributed (normal distribution was tested using the Shapiro-Wilk test) or median with interquartile range. Statistical analyses were performed using the χ² test or the exact Fisher test for comparison of categorical variables and the Student's t-test or the Mann-Whitney U test for continuous variables. A p-value below 0.05 was considered statistically significant (tests two-sided). Association of variables and score was evaluated fitting ordinal logistic regression models.
STATA statistical package (release 15.1, 2017, Stata Corporation, College Station, Texas, USA) was used to perform the data analysis.

Pelizzo G., Bussani R., De Silvestri A., Di Mitri M., Rosone G., Amoroso S., Milazzo M., Girgenti V., Mura G.B., Unti E., Rozze D., Shafiei V, & Calcaterra V. (2020). Laparoscopic Cholecystectomy for Symptomatic Cholecystic Disease in Children: Defining Surgical Timing. Frontiers in Pediatrics, 8, 203.

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Systematic Review of Diabetes Treatments

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For continuous variables, such as the change from baseline in HbA1c, FPG, 2‐h PPG levels and bodyweight, weighted mean difference (WMD) with 95% confidence intervals (CIs) between treatment groups were calculated. For the analysis of dichotomous outcomes, such as the risk of hypoglycemia, relative risks (RR) and their corresponding 95% CIs were calculated. The random effects model of meta‐analysis was used to calculate pooled WMDs or RRs, 95% CIs and P‐values, with P < 0.05 considered statistically significant. We also calculated the I² statistic, which is an indicator of heterogeneity across the included studies in percentages. The presence of publication bias for the primary outcome was investigated graphically using a funnel plot along with Egger's test for funnel plot asymmetry. We used the Stata statistical package for all analyses (version 12; StataCorp, College Station, Texas, USA).

Min S.H., Yoon J., Hahn S, & Cho Y.M. (2017). Efficacy and safety of combination therapy with an α‐glucosidase inhibitor and a dipeptidyl peptidase‐4 inhibitor in patients with type 2 diabetes mellitus: A systematic review with meta‐analysis. Journal of Diabetes Investigation, 9(4), 893-902.

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Cancer Case Forecasting and Regression

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Statistical analysis was carried out using the STATA statistical package (version 13, STATA Corporation, TX, USA) and Excel Version 10. We descriptively summarized the data. We used a constant growth of the cancer cases assumption to predict cancer cases estimates for the future years using linear regression analysis. Additionally, we carried out forecasting of new cases by predicting the 2016–2030 cases based on the 1999–2015 numbers. We modeled the new case in relation to year diagnosis by constructing a trend line because the reported cases showed an upward trend. Then, we forecast the existing by adding a trend line to existing data points to allow for extrapolation of future new cases. The forecast future values were based on the trend and the possibility of the regression line exhibiting other non-linearity characteristics were ruled out because of the linearity of data.

Jazieh A.R., Da’ar O.B., Alkaiyat M., Zaatreh Y.A., Saad A.A., Bustami R., Alrujaib M, & Alkattan K. (2019). Cancer Incidence Trends From 1999 to 2015 And Contributions Of Various Cancer Types To The Overall Burden: Projections To 2030 And Extrapolation Of Economic Burden In Saudi Arabia. Cancer Management and Research, 11, 9665-9674.

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Cluster-Randomized Nursing Home Trial

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Participating NHs were clustered according to their physicians (some physicians attend multiple NHs) to prevent contamination; clusters were then randomised by between the intervention and control groups (ratio 1:1), using a random list generated by the investigators with the Stata statistical package (v15, Stata Corp, College Station LLC, TX, USA). The investigators informed NHs of allocation by e-mail after randomisation.
Given the nature of the intervention, neither the investigators, NHs, nor healthcare professionals were blinded; only the statistician performing the analysis was blinded.

Cateau D., Ballabeni P, & Niquille A. (2021). Effects of an interprofessional Quality Circle-Deprescribing Module (QC-DeMo) in Swiss nursing homes: a randomised controlled trial. BMC Geriatrics, 21, 289.

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Dietary GHG Emissions by Diet Group

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The arithmetic means and standard deviations of dietary GHG emissions for each diet group were calculated. An ANOVA was conducted to estimate GHG emissions by diet group adjusted for sex and age, categorised in 10 years age bands from 20–29 to 70–79. Statistical significance was set at the 5 % level and all analyses were conducted using the Stata statistical package (StataCorp 2011 ).

Scarborough P., Appleby P.N., Mizdrak A., Briggs A.D., Travis R.C., Bradbury K.E, & Key T.J. (2014). Dietary greenhouse gas emissions of meat-eaters, fish-eaters, vegetarians and vegans in the UK. Climatic Change, 125(2), 179-192.

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Biomechanical Exposures and Carpal Tunnel Syndrome

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Correlations between exposures were estimated using the Spearman rank correlation coefficient. Hazard ratios (HR) between exposures and incident CTS were estimated using Cox proportional hazards regression with robust confidence intervals. Guided by DAGs, the models were adjusted for potential confounding by personal factors related to both exposure and outcome that were not on the causal pathway. Using the forward stepwise procedure, variables were retained in the model if inclusion resulted in a change of the effect estimate of the primary exposure variable by 10% or more^{17 (link)}. Ultimately, age, gender, BMI and study site were included in all models. Models where specific biomechanical exposures were the primary exposure of interest were adjusted for dissimilar biomechanical exposures (ie., exposures of a different type)^{9 (link)}. For example, the relationship between Peak Hand Force and CTS was adjusted for Total Repetition Rate and wrist posture whereas the model assessing the relationship between Forceful Repetition Rate and CTS was only adjusted for wrist posture. Assessment of confounding of one class of exposures by another (e.g., biomechanical, work psychosocial) used the same process and criteria described above. All analyses were implemented with the Stata statistical package (Stata, College Station, TX).

Harris-Adamson C., Eisen E.A., Neophytou A., Kapellusch J., Garg A., Hegmann K.T., Thiese M.S., Dale A.M., Evanoff B., Burt S., Bao S., Silverstein B., Gerr F, & Rempel D. (2016). Biomechanical and psychosocial exposures are independent risk factors for Carpal Tunnel Syndrome: assessment of confounding using causal diagrams.. Occupational and environmental medicine, 73(11), 727-734.

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Genetic Factors Influencing Clinical Outcomes

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Statistical analysis was conducted using the STATA statistical package (version 10.1, STATA Corp., College Station, TX). T-test, X² test or Fisher's exact test were used to evaluate whether the distribution of genotype frequencies of CYP3A4*1B, PXR-HNF4, and PXR-HNF3β varied among cases and controls. For the comparison of clinical characteristics in the case group, ORs were calculated as an estimate of relative risk and 95% confidence intervals (CIs) were calculated using a bivariate logistic model. A value of p<0.03 was considered statistically significant after multiple testing adjustment by using the Bonferroni correction. The X² test was also used to assess deviations of allelic frequencies from Hardy-Weinberg equilibrium. The interaction between alleles was analyzed using the software Plink V 1.07. The number (n) of the case population for each association with clinical characteristics is indicated in the tables.
The sensitivity and specificity from significant models were estimated [23] (link). A cutoff of 50% for the classification of the event and the Receiver Operating Characteristic (ROC curve) were used. All calculations were performed using STATA as post logistic model estimation.

Reyes-Hernández O.D., Vega L., Jiménez-Ríos M.A., Martínez-Cervera P.F., Lugo-García J.A., Hernández-Cadena L., Ostrosky-Wegman P., Orozco L, & Elizondo G. (2014). The PXR rs7643645 Polymorphism Is Associated with the Risk of Higher Prostate-Specific Antigen Levels in Prostate Cancer Patients. PLoS ONE, 9(6), e99974.

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Genetic Associations with Gastric Cancer

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Pearson's chi-square test was used to examine the differences in demographic variables, smoking status, drinking status and genotype distributions of all 30 SNPs between GCA cases and controls. The associations between telomere length-related SNPs and GCA risk were estimated by odds ratios (ORs) and their 95% confidence intervals (95% CIs) computed by logistic regression models. All ORs were adjusted for age, sex, smoking or drinking status, where it was appropriate. The association of SNPs with telomere length was assessed using linear regression adjusted for age, sex, smoking and drinking status. During meta-analyses, a fixed effect model (the Mantel-Haenszel method) was performed to calculate the combined OR using Stata Statistical package (version 11.0; Stata Corp.). Bonferroni correction was used for multiple comparisons. All statistical tests were two-sided and were performed using SPSS 16.0 (SPSS Inc.).

Zhang N., Zheng Y., Liu J., Lei T., Xu Y, & Yang M. (2019). Genetic variations associated with telomere length confer risk of gastric cardia adenocarcinoma. Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 22(6).

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