The SPSS V.18.0 software (IBM SPSS Statistics, IBM Corporation, Armonk, New York, USA) for MS Windows and STATA V.15.0 were used for the statistical analysis. Categorical variables were presented as absolute numbers or percentages and continuous data as means (SD). The two subgroups were compared using t-tests, and multivariable analyses for the risk factors of erosive oesophagitis were conducted. Additionally, we have analysed our data with two-stage least square estimation using the Baltagi-Chang one-way model (STATA V.15) to clarify unobserved confounding variable. Statistical analysis using two independent sample t-tests was performed. P values <0.05 were considered statistically significant.
Stata v 15
Manufactured by StataCorp
Sourced in United States, Austria, Australia
Stata V.15 is a comprehensive statistical software package that provides a wide range of data analysis and management tools. It offers features for data manipulation, statistical modeling, graphics, and programming. Stata V.15 is designed to handle complex data structures and provide advanced analytical capabilities for researchers, analysts, and professionals across various fields.
Automatically generated - may contain errors
Lab products found in correlation
Sas v9, by SAS Institute (12 mentions)
Stata statistical software release 15, by StataCorp (11 mentions)
Spss v25, by IBM (4 mentions)
Prism v9, by GraphPad (4 mentions)
Stata statistical software, by StataCorp (4 mentions)
Prism 8, by GraphPad (4 mentions)
Sas software, by SAS Institute (4 mentions)
Spss v23, by IBM (3 mentions)
Spss v 22, by StataCorp (3 mentions)
Prism 7, by GraphPad (3 mentions)
Spss v27, by IBM (3 mentions)
Stata 15, by StataCorp (2 mentions)
Prism 6.0f, by GraphPad (2 mentions)
Spss v18, by IBM (2 mentions)
Excel v 2016, by StataCorp (2 mentions)
Prism v8, by GraphPad (2 mentions)
Excel 2016, by StataCorp (2 mentions)
Sas v9, by StataCorp (2 mentions)
Spss v22, by IBM (2 mentions)
R software version 3, by R Project for Statistical Computing (2 mentions)
1 057 protocols using stata v 15
1
Analysis of Erosive Oesophagitis Risk Factors
2
Exploring Cessation Barriers in Cancer Centers
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Responses were collected anonymously, and once the data collection process was completed, data were imported from Qualtrics into Stata (V.15.0, College Station, Texas, USA: StataCorp LLC) for analysis purposes. CCPs not reporting dedication to patient care (n=17) were excluded for analytic purposes. Study variables were summarised, in aggregate, using standard descriptive statistics such as mean, SD, frequency and proportion. Relevant variables were compared between cancer centres using χ2, Fisher’s exact and two-sample t-tests, when appropriate. In an exploratory analysis, CCPs' characteristics and reported barriers were used to predict the odds of providing cessation treatment or referral to patients with cancer at the initial visit using logistic regression for each study site. We did not conduct any variable selection, such as stepwise selection, because such approaches are known to lead to biased coefficient estimation.17 The significant threshold was set at 0.05. All analyses were conducted using Stata V.15.1 (StataCorp, College Station, Texas, USA).
3
Factors Associated with Sensitive Topics
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Baseline parameters and outcomes were stratified among patients with and without sensitive topics. Logistic regression analyses were conducted to evaluate factors associated with sensitive topics. Furthermore, univariable linear and logistic regression models were conducted to investigate associations of sensitive topics with primary and secondary endpoints. We additionally calculated multivariable models adjusted for study centre and randomisation arm (bedside or hallway). In the subgroup of patients with sensitive topics, potential risk factors for low satisfaction compared with high satisfaction were assessed using Student’s t-test. We used Stata V.15.0 (StataCorp, College Station, Texas, USA) for all statistical analyses. A p value of <0.05 (two-tailed) was considered statistically significant. Stata V.15.0 (StataCorp, College Station, Texas, USA) was used for all analyses.
4
Complex Survey Data Analysis
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Data were analyzed using STATA v.15. All analyses were performed using weights that reflected the likelihood of selection at each stage of sampling [36 ], and all statistics represent weighted data. Missing values were low (<2% for each variable); cases with missing values were not included in analyses.
Weights were calculated for each city separately. The study used a two-stage cluster sampling; sampling weight was calculated based on the sampling probability separately for each sampling stage and cluster. [41 (link)] All analyses used the svy functions in STATA v. 15, that adjust for complex survey sampling. [42 ]
Weights were calculated for each city separately. The study used a two-stage cluster sampling; sampling weight was calculated based on the sampling probability separately for each sampling stage and cluster. [41 (link)] All analyses used the svy functions in STATA v. 15, that adjust for complex survey sampling. [42 ]
5
Spatial Analysis of Child Immunization
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Children immunization data and other variables relevant to the study were extracted from the child recode file (PKKR71.DTA). In geographical coordinate file, cluster data were used from 561 clusters or enumeration blocks (EBs). In the geographic coordinate data file 535th enumeration block was recorded as 0, so this EB was dropped from the study. After getting data, data extraction, data weighting, and data recording were carried out using STATA V.15 while for spatial analysis data were managed using Excel and STATA V.15. Following is the sampling procedure layout.
6
Diagnostic Accuracy Evaluation for sCJD
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The sensitivity and specificity were calculated for sCJD and IPD groups, and statistical significance between groups was determined by Fisher's Exact Test (Stata, v15.1). Mixed effect linear modelling of MRC Scale scores was undertaken with Stata, v15.1.
7
Bayesian Network Meta-Analysis of Acupuncture Interventions
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The network meta-analysis will be conducted using Aggregate Data Drug Information System (ADDIS) V.1.16.8 software (Drugis, Groningen, NL) [43 (link), 45 (link)]. A random effects model will be applied to perform a Bayesian network meta-analysis to integrate the direct and indirect evidence with the Markov Chain Monte Carlo method. Four chains will be applied as parameters for simulation. There are 50,000 simulation iterations in each chain, and the researcher will discard the first 20,000 simulations to remove the effect of the initial value. Visual inspection of the trace plots will be used to appraise model convergence and taking the Gelman-Rubin statistic into consideration. In the meantime, the network diagram will be generated using STATA V.15.0 software (Stata Corp., College Station, TX, USA) and compare each outcome. In light of each specific outcome, the effects of different acupuncture approaches will be sequenced to display the most effective surface under the cumulative ranking curve and mean ranks with their 95% CI.
8
Statistical Analysis of Dichotomous and Continuous Outcomes
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The meta-analysis will be conducted using Stata V.15.0 software (Stata). For dichotomous variables, the results will be reported using RR and 95% CIs. For continuous variables, if the assessment tools used in the included RCTs are consistent, the outcomes will be expressed as weighted mean difference and 95% CI. However, if different assessment tools are used, the final results will be presented as standardised mean difference and 95% CI.
9
Meta-analysis of Outcome Indicators
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If multiple RCTs include the same outcome indicator, we will extract the data and conduct a meta-analysis using Stata V.15.0 software (Stata). However, if only a single literature or outcome is available, we will perform a descriptive analysis without conducting a meta-analysis.
10
Observational Cohort Study of DVT/PE Treatment
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The analysable cohort for this article were those patients treated for DVT or PE and prevention of recurrent DVT and PE only. Incident reports were calculated on treatment (+5 drug half-lives (3 days) after stopping to account for drug elimination) during the 12 weeks of observation. Patients were censored according to the first of the following dates: end of the 12-week observation period, loss to follow-up, death, first report of stopping treatment (+5 drug half-lives) or first report of outcome of interest. A Kaplan-Meier curve for the time-to-treatment cessation was produced, including the number of patients at risk. Statistical analyses of baseline data were descriptive, exploratory and largely limited to frequency tables or summary statistics (eg, median+quartiles). Primary and secondary outcome measures are presented as unadjusted cumulative incidence (risk) and incidence rates (per 100 patient-years) with corresponding 95% CIs. Data were analysed using STATA V.15.0 software (StataCorp, College Station, Texas, USA).
The study used the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) cohort reporting guidelines.11 (link)
The study used the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) cohort reporting guidelines.11 (link)
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