Quantitative data were analysed using frequencies, percentages and chi-squared analyses. Confidence intervals for proportions were calculated at the 95% level. Qualitative results from free text boxes were analysed using content analysis to identify commonalities. The aim of collecting free text qualitative data was to allow respondents to provide additional descriptive detail or context to answers. Microsoft Excel, IBM SPSS 26 and Stata SE 16 were used for analysis and to generate graphs, with confidence intervals calculated in Stata SE 16 and chi square tests computed in IBM SPSS 26.
Stata se 16
Manufactured by StataCorp
Sourced in United States, Austria, United Kingdom
Stata/SE 16 is a data analysis and statistical software package developed by StataCorp. It is designed to provide advanced statistical capabilities for handling large and complex datasets. Stata/SE 16 offers a range of data management, statistical modeling, and graphical tools to support researchers, analysts, and professionals in various fields.
Automatically generated - may contain errors
Lab products found in correlation
Sas 9, by SAS Institute (8 mentions)
Spss 26, by IBM (3 mentions)
Prism 7, by GraphPad (2 mentions)
Spss version 25, by IBM (1 mentions)
Spss version 23, by StataCorp (1 mentions)
For windows version 19, by MedCalc (1 mentions)
Mplus version 8, by IBM (1 mentions)
Spss version 24, by IBM (1 mentions)
Prism 8, by GraphPad (1 mentions)
Stata, by StataCorp (1 mentions)
Spss version 22, by IBM (1 mentions)
Spss statistics 25, by StataCorp (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Sigmaplot, by Grafiti LLC (1 mentions)
Nvivo version 12, by Lumivero (1 mentions)
Prism 6, by GraphPad (1 mentions)
Stata statistical software version 16, by StataCorp (1 mentions)
Program, by MedCalc (1 mentions)
Excel 2016, by StataCorp (1 mentions)
T test calculator, by GraphPad (1 mentions)
314 protocols using stata se 16
1
Quantitative and Qualitative Analysis
2
Evaluation of Healthcare Facility Capabilities in Africa
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Facilities were included in the analysis if located in Africa, and with completed self-assessment questions. We analysed the level of compliance (proportions) with the 31 self-assessment questions/capability criteria classified in four levels: NC, PC, FC, and NA; and the availability status of 23 medical supplies in the healthcare centres that completed the ‘Supplies checklist’ answered as: Yes, No, and NA. In addition, we assessed differences in supplies and capabilities scores depending on the location, type, and ownership of the facilities. The scores’ data were not normally distributed. Hence, we performed Wilcoxon-Mann-Whitney rank-sum tests for two-group comparisons, and Kruskal-Wallis tests for multiple group comparisons, with significant level α = 0.05. When the Kruskal-Wallis test was rejected, a post hoc Dunn’s test was performed for pairwise multiple comparisons with Bonferroni adjustment, with family-wise error rate (FWER) = 0.05, and a significant level α = 0.05/total number of tests. Data was analysed in Stata/SE 16, and graphs were generated in Microsoft® Excel® for Microsoft 365 for MSO and Stata/SE 16.
3
Fiber and Pore Characterization Protocol
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The acquired data of the fiber diameter, pore size, and infiltration depth were analyzed with one-way ANOVA using Stata/SE 16.1. The contact angle data were plotted as mean values and error bars corresponding to one standard deviation above and below the corresponding mean value. The box plots and histograms presented in the next section were created with Stata/SE 16.1.
4
Statistical Analysis of Meta-Analysis Data
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All statistical analyses of the data were performed using Review Manager (RevMan) 5.3 software and Stata/SE 16.1 software. Sensitivity analysis was performed by omitting the literature one by one to determine whether the results were stable, and the publication bias of this meta-analysis was evaluated using the Begg test according to Stata/SE 16.1 software. The Q test and I2 statistics were applied to estimate the heterogeneity of the results. A fixed-effects model was selected when an I2 < 50% was observed. The synthetic estimate was calculated based on the random-effects model when heterogeneity was evident (I2 > 50%). Statistical significance was set at P < .05.
5
Exploratory Statistical Analysis of Therapy
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All analyses were performed on an exploratory basis and the results will be interpreted purely descriptively. Continuous variables were summarized with the use of descriptive statistical measures [median and interquartile range (25th, 75th percentile)] and categorical variables were displayed as frequency tables (n, %). Standard statistical tests were used to check univariate associations between categorical variables and therapy (Fisher’s exact tests) or continuous variables and therapy (ANOVA). For time-to-event endpoints, Kaplan–Meier estimates were used to describe and visualize the effect of categorical variables. DFS, CSS and OS were calculated from the date of first course of chemotherapy. Patients without event (recurrence and death, respectively) were censored on the date of last contact. Log rank tests have been used to explore the prognostic value of categorical variables in clinical outcomes. Stratified version of Log rank tests was also performed to control for therapy. The level of 5% was used for statistical significance. All statistical analyses were performed using STATA/SE 16.1 software (Copyright 1985–2019; StataCorp LP, College Station, Texas, USA).
6
Exploratory Analysis of Patients' Data
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Patients’ data were analyzed on an exploratory basis. Continuous variables are summarized with the use of descriptive statistical measures [median and interquartile range (IQR; 25th, 75th percentile)], and categorical variables are displayed as frequency tables (n, %). Statistical tests used to check univariate associations between categorical or continuous variables and outcomes were Pearson’s chi-squared test, Fisher’s exact test, t-test, or Wilcoxon rank-sum test as appropriate. Box plots are used to visualize the laboratory data at admission and at their highest/lowest value. The level of 5% was used for statistical significance. All statistical analyses were performed using STATA/SE 16.1 software (Copyright 1985–2019; Stata Corp LP, College Station, TX, United States).
7
Evaluating Impact of Comorbidity Scores
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Continuous data were presented as mean ± standard deviation or median (range), while categorical data were displayed as absolute or relative frequencies. The Student’s t-test was used for the analysis of continuous variables. Fisher’s exact test or the χ2 test was used for categorical data. Furthermore, Kaplan–Meier analysis was conducted to examine long-term follow-up data, encompassing recurrence and mortality. Statistical significance was defined as a p-value less than 0.05. All statistical analyses were conducted using IBM SPSS version 25 (IBM Corp., Armonk, NY, USA).
Experiments were conducted using restricted cubic splines with three knots. A restricted cubic spline curve was used to show the predicted probability (solid line) and 95% confidence intervals (shades) to evaluate the association of CCI and ACCI with complete resection and complication rates. Statistical analyses were conducted using Stata/SE 16.1 software (StataCorp LLC., College Station, TX, USA).
Experiments were conducted using restricted cubic splines with three knots. A restricted cubic spline curve was used to show the predicted probability (solid line) and 95% confidence intervals (shades) to evaluate the association of CCI and ACCI with complete resection and complication rates. Statistical analyses were conducted using Stata/SE 16.1 software (StataCorp LLC., College Station, TX, USA).
8
Comprehensive Biomarker Analysis Protocol
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Stata/SE 16.1 software (StataCorp LP, College Station, TX, United States) was used to analyze the statistical data. Statistics were considered significant when p-value <0.05. All study variables were subjected to descriptive statistics analysis, which was provided as frequency (%) for categorical data and mean ± standard deviation (SD) or median for nonnormal quantitative data. One-way analysis of variance (ANOVA) statistic and post hoc analysis using the Scheffe test with p-value <0.05 were utilized if the distribution of the quantitative data, such as age and laboratory results, was normal. The Kruskal-Wallis test and post hoc Mann–Whitney U test with p-value <0.017 were used if the data distribution was not normal.
The following Spearman’s correlation coefficient was analyzed; (1) anthropometric measurements, including BMI and waist circumference (2) physical examination; blood pressure and (3) blood tests; FBS, HbA1C, TC, TG, LDL-C, HDL-C, AST, ALT, and hs-CRP. Correlation heat map visualization was performed using the ggplot2 R package. A p-value <0.05 was considered statistically significant and was labeled in the figure. In addition, the phylogenetic heat tree was visualized using the metacoder R package.
The following Spearman’s correlation coefficient was analyzed; (1) anthropometric measurements, including BMI and waist circumference (2) physical examination; blood pressure and (3) blood tests; FBS, HbA1C, TC, TG, LDL-C, HDL-C, AST, ALT, and hs-CRP. Correlation heat map visualization was performed using the ggplot2 R package. A p-value <0.05 was considered statistically significant and was labeled in the figure. In addition, the phylogenetic heat tree was visualized using the metacoder R package.
9
Survival Analysis of Cancer Patients
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Clinical variables were analyzed for association with BMT using univariable logistic regression and multivariable logistic regression. CROSS trial eligibility was excluded from multivariable analysis to prevent collinearity with variables included in criteria. Similarly, age was excluded from multivariable analysis in favor of age-adjusted Charlson Comorbidity Index (CCI), given that these were highly correlated (18 (link)).
OS was analyzed using log-rank test and Cox proportional hazards regression, with initial timepoint recorded as the diagnostic biopsy date of the tumor. The proportional hazards assumption was validated graphically by using log-log survival plots. Disease-free survival (DFS) and relapse-free survival (RFS) were generated in the same fashion. Multivariate logistic regression was utilized to assess variables, including modality, for association with OS.
All data analysis was conducted using STATA/SE 16.1 software (Stata, RRID:SCR_012763) of dataset generated as described above (19 ).
OS was analyzed using log-rank test and Cox proportional hazards regression, with initial timepoint recorded as the diagnostic biopsy date of the tumor. The proportional hazards assumption was validated graphically by using log-log survival plots. Disease-free survival (DFS) and relapse-free survival (RFS) were generated in the same fashion. Multivariate logistic regression was utilized to assess variables, including modality, for association with OS.
All data analysis was conducted using STATA/SE 16.1 software (Stata, RRID:SCR_012763) of dataset generated as described above (19 ).
10
Telemedicine Adoption Factors Analysis
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Multiple logistic regression analysis was performed to assess the associations between occupational factors and the use of telemedicine. As the dependent variable, the prevalence of new and existing telemedicine users was analyzed cross-sectionally. The multivariate model included age, sex, marital status, education, work area, and annual household income. The results are presented as adjusted odds ratios (aORs), 95% confidence intervals (CIs) and two-sided P values. P < 0.05 was considered statistically significant. All statistical analyses were performed using Stata/SE 16.1 software (StataCorp, College Station, TX, USA).
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