Stata software

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Stata is a general-purpose statistical software package for data analysis, data management, and graphics. It provides a wide range of data manipulation, statistical, and graphical tools to help researchers and analysts effectively analyze and visualize data.

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Stata (4799 citations) STATA software version 11.0 (222 citations) Stata software v.11.2 (32 citations) STATA software V.14.0 (29 citations) Stata MP software, Version 16.1 (11 citations) Stata 14.2 statistical software (9 citations) Stata 16.0 statistical software (9 citations) Stata statistical software package, version 16 (7 citations) Stata version 13.1 statistical software (6 citations) Stata 11.2 statistical software (6 citations)

2 071 protocols using stata software

Goodness-of-fit and multicollinearity analysis

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Goodness‐of‐fit (calibration) for the models was assessed with the Hosmer–Lemeshow test. Multi‐collinearity between the variables in the model was assessed by calculation of the variance inflation factor. All statistical analyses were performed using Stata software (version 16.0, StataCorp, College Station, Texas, USA). Instrumental variable 2‐stage least squares regression models were completed using the IVREG2 module.²³ All tests were 2‐tailed, and a P<0.05 was considered statistically significant. Because of multiple analyses, P<0.05 was expected to occur by chance in 1 out of 20 analyses. The validity of instrumental variables was examined by calculation of the standardized difference of variables that reflects known patient’s characteristics and procedural details in treated and untreated patients stratified on the calendar year during the study period. We used logistic regression to evaluate the predictive power of instruments for treatment with P2Y₁₂ antagonists as well as for primary and secondary outcomes. All tests were 2‐tailed, and P<0.05 was considered statistically significant. All statistical analyses were performed using Stata software (version 16.0, StataCorp, College Station, Texas, USA). All tests were 2‐tailed and a P<0.05 was considered statistically significant.

Völz S., Petursson P., Odenstedt J., Ioanes D., Haraldsson I., Angerås O., Dworeck C., Hirlekar G., Myredal A., Albertsson P., Råmunddal T., Redfors B, & Omerovic E. (2020). Ticagrelor is Not Superior to Clopidogrel in Patients With Acute Coronary Syndromes Undergoing PCI: A Report from Swedish Coronary Angiography and Angioplasty Registry. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 9(14), e015990.

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Bayesian Network Meta-analysis of Art Therapies

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We used mean difference (MD) and its 95% credible interval (CrI) to describe the results of continuous data. Considering the expected heterogeneity between studies, we conducted random-effects network meta-analyses for each outcome within a Bayesian framework by using Markov Chains Monte Carlo in R software (version 4.1.3). The relative efficacy of art therapies was estimated in accordance with the surface under the cumulative ranking curve (SUCRA) values. The network plots for each outcome were drawn in Stata software (version 15.0). The Egger’s test was used to evaluate the presence of publication bias in Stata software (version 15.0) when more than nine studies were included in analysis (17 (link)). The node-splitting analysis was used to assess the inconsistency if the outcome was a closed loop. Heterogeneity was evaluated with the I²statistics, and the I² values were considered as none (0%), low (25%), moderate (50%) and high (75%) (18 (link)). To conduct the sensitivity analyses, we removed the study with the smallest sample size for each outcome. And then, the same methods to conduct Bayesian network analysis were repeated. The quality of evidence was evaluated by two authors based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework (19 (link)).

Liu Q., Wang F., Tan L., Liu L., Cheng H, & Hu X. (2023). Comparative efficacy of various art therapies for patients with dementia: A network meta-analysis of randomized controlled trials. Frontiers in Psychiatry, 14, 1072066.

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Epidemiology of GBS Colonization in Pregnant Women

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Data analysis was performed using Stata Software. Bivariate analysis was performed using Chi-Square and Fisher’s Exact to evaluate the association between risk factors to colonization of GBS in pregnant woman. Logistic regression was performed to compute Odds Ratio (OR) with 95% Confidence Interval (CI).
Power of the test was computed using Stata Software. Previous study found the prevalent of GBS colonization in pregnant woman in Indonesia was 31% (P₀ = 0.31) [10 ] and we estimated an increase of 10% (∂ = 0.1) therefor P_a was 41%. Using these parameters, we found the power of the test for a sample size of 179 with alpha 0.05 was 74.3%.

Safari D., Gultom S.M., Tafroji W., Azzahidah A., Soesanti F., Khoeri M.M., Prayitno A., Pimenta F.C., da Gloria Carvalho M., Uiterwaal C.S, & Putri N.D. (2021). Prevalence, serotype and antibiotic susceptibility of Group B Streptococcus isolated from pregnant women in Jakarta, Indonesia. PLoS ONE, 16(5), e0252328.

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Multimodal MRI Evaluation of Degenerative Cervical Myelopathy

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Statistical analysis was performed using Stata software (version 16; StataCorp LP, College Station, TX). Mean age was higher in DCM patients compared to healthy controls (DCM, 54.9 ± 11.0; healthy control, 41.6 ± 15.0; p = 0.001 [mean ± SD]); thus, age was considered as a covariate of no interest in our linear regression models. Macrostructural morphometric measurements (i.e., SCA, GMA, and WMA) were compared between healthy controls and DCM patients by means of a one-tailed t-test. Next, microstructural DTI indices (FA, AD, MD, and RD) within the cervical and lumbar cord were compared between groups using a two-sample t-test (unpaired, onetailed, p < 0.05). Patients were divided into two groups depending on the presence versus absence of T2-hyperintensity signal in the cervical cord as a sign of cervical myelopathy. Measurements of MSCC and MCC, volumetric and microstructural MRI readouts, as well as clinical scores were compared between groups using a two-sample t-test (unpaired, one-tailed; p < 0.05). Finally, linear regression and Pearson's correlation analysis in Stata software (StataCorp LP) were performed to reveal possible relationships between volumetric and microstructural MRI readouts in cervical and lumbar cord and clinical and electrophysiological outcomes, adjusted for age, using a level of significance set to p < 0.05.

Vallotton K., David G., Hupp M., Pfender N., Cohen-Adad J., Fehlings M.G., Samson R.S., Wheeler-Kingshott C.A.M.G., Curt A., Freund P, & Seif M. (2021). Tracking White and Gray Matter Degeneration along the Spinal Cord Axis in Degenerative Cervical Myelopathy. Journal of neurotrauma, 38(21).

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Prognostic Value of Ferritin Staining

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Univariate and multivariate Cox regression analyses were performed using Stata software (version 12.0). Hazard ratios (HR) and the corresponding 95% confidence interval (CI) were calculated based on stratified groups of patients. Kaplan-Meier analyses were performed using Stata software (version 12.0) and GraphPad Prism 5 (version 5.01). Based on visual observation of Kaplan-Meier curves, optimal cutoffs of cFTH1 and nFTH1 staining were determined for reporting. Nonparametric Spearman correlation and Wilcoxon rank-sum test were used for analyzing IHC staining data.

Liu N.Q., De Marchi T., Timmermans A.M., Beekhof R., Trapman-Jansen A.M., Foekens R., Look M.P., van Deurzen C.H., Span P.N., Sweep F.C., Brask J.B., Timmermans-Wielenga V., Debets R., Martens J.W., Foekens J.A, & Umar A. (2014). Ferritin Heavy Chain in Triple Negative Breast Cancer: A Favorable Prognostic Marker that Relates to a Cluster of Differentiation 8 Positive (CD8+) Effector T-cell Response. Molecular & Cellular Proteomics : MCP, 13(7), 1814-1827.

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Metabolic Syndrome Predictors Analysis

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Data analysis in the present study was performed using STATA software (version 14.0, Stata Corp, College Station, TX, USA). Qualitative data were reported as numbers and percentages. Considering the large sample size, the distribution of all variables in our study was normal and quantitative data were reported as averages and standard deviations. Comparison between qualitative data between the two groups was done using chi‐squared test and comparison between quantitative variables between the two groups was done using independent t‐test. Univariate and multivariate regression analyses were performed to identify the predictors of metabolic syndrome in patients.
ROC analysis was performed using STATA software (version 14.0, Stata Corp) to determine the sensitivity and specificity of the main variables in predicting metabolic syndrome in patients. The p values lower than .05 were considered statistically significant.

Yazdi F., Baghaei M.H., Baniasad A., Naghibzadeh‐Tahami A., Najafipour H, & Gozashti M.H. (2021). Investigating the relationship between serum uric acid to high‐density lipoprotein ratio and metabolic syndrome. Endocrinology, Diabetes & Metabolism, 5(1), e00311.

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Platelet Count Evaluation in Treatments

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Quantitative values are reported as median (1^st-3^rd interquartile) and qualitative data as percentage. P-values were derived using Chi² test and Mann-Whitney test for qualitative and quantitative values, respectively. Wilcoxon matched pairs test was used to compare platelet count before and after treatments. P<0.05 was considered significant. Statistical analyses were performed with STATA_TM Software (Stata Corporation).

Estève C., Samson M., Guilhem A., Nicolas B., Leguy-Seguin V., Berthier S., Bonnotte B, & Audia S. (2017). Efficacy and safety of dapsone as second line therapy for adult immune thrombocytopenia: A retrospective study of 42 patients. PLoS ONE, 12(10), e0187296.

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Handling Missing Data Using Multiple Imputation

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Missing data were related to smoking history, body mass index at admission, Killip classification, Barthel Index score at admission, and Barthel Index score at discharge. Therefore, we used the multiple imputation (MI) method to handle missing data that were assumed to be missing at random [29] . We applied 20 imputed datasets using all covariates and outcomes using the MI chained equations technique for missing data. After using the "MI impute chained" command in Stata TM software (StataCorp., College Station, TX, USA) [30] , we combined the imputation estimate and standard errors based on Rubin's rule [31] .

Nakamura K., Ohbe H., Uda K., Fushimi K, & Yasunaga H. (2021). Early rehabilitation after acute myocardial infarction: A nationwide inpatient database study. Journal of cardiology, 78(5).

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Sociodemographic Predictors of Weight Management Practices

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STATA software¹⁷ was used for all analyses. Sociodemographic characteristics between men with and without children were compared using Pearson chi-square test. Unadjusted and adjusted odds ratios with 95% CI using logistic regression were calculated to examine the association between sociodemographic predictors and providers’ weight-related practices. Sociodemographic variables were retained in the regression analyses if they demonstrated a significant bivariate association with the outcome (p<0.1) or because prior research demonstrated significant associations with patient-provider interactions. Race/ethnicity and socioeconomic status were retained in the model for this reason.¹⁸ Statistical significance was set at p<0.05. Analyses were weighted using survey settings to generate nationally representative descriptive statistics and odds ratios. Missing data were imputed using sequential regression and logical imputation methods per the NSFG design and data collection methods.¹⁶ Regression imputation was used for 9.6% of cases for socioeconomic status and less than 2% for all other variables. Missing data for weight-related outcomes (<1%) were not included in imputation analysis and these cases were not included in the analysis.

Boykin A., Demirci J, & Miller E. (2020). Investigating the Relationship between Fatherhood and Provider Weight-Related Practices and Counseling in Clinical Settings. Journal of nutrition education and behavior, 52(7), 713-718.

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Respiratory Syncytial Virus Detection Protocol

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A total of 45 samples were assessed for RSV expression and a minimum of 11 samples were used to compare RSV plaque assay formation in each treatment group. All samples were run in duplicate and all experiments completed at least twice to ensure reproducibility. Odd’s ratios and relative risk for RSV detected in cord samples were calculated using cross-tabulations with Fishers exact analysis. Due to an absence of data regarding the prevalence of RSV in CBMs, post-hoc power analysis was used to assess if the study interpretations were feasible based on the sample size available. The observed power was calculated at 82% based on the relative risks of RSV detection and sample size used. In order to mitigate concerns surrounding use of observed power and demonstrate the precision of these findings, confidence intervals are reported for the effect sizes calculated. Due to the small sample size and skewed distribution of the dataset, Mann Whitney-U analysis was used to compare RSV expression between birth seasons, and to assess differences in RSV plaque numbers between non-treated and treated co-cultured cells. Stata software by StataCorp (College Station, Texas, USA) was used to complete graphs and statistical analysis for this study with significance taken as p = <0.05 for two-tailed tests. Graphs represent median values with interquartile ranges unless stated otherwise.

Fonceca A.M., Chopra A., Levy A., Noakes P.S., Poh M.W., Bear N.L., Prescott S, & Everard M.L. (2017). Infective respiratory syncytial virus is present in human cord blood samples and most prevalent during winter months. PLoS ONE, 12(4), e0173738.

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