The inter-judge agreement was determined by their agreement percentage and Kappa-test. The reliability of the questionnaire was estimated by its internal consistency, measured by Cronbach’s alpha, and by its temporal stability, measured by the agreement percentage and by the Kappa-index reached in the test-retest method. Data were analyzed using a statistical software (Stata statistical software v.13, StataCorp, LP, College Station, TX, USA) and a statistical significance was considered when p<0.05.
Stata statistical software v 13
Manufactured by StataCorp
Sourced in United States
Stata is a general-purpose statistical software package that provides a wide range of data analysis and visualization tools. Version 13 includes a comprehensive set of features for data management, statistical modeling, and reporting. The software is designed to be user-friendly and efficient for researchers, analysts, and professionals working with large and complex datasets across various fields.
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Lab products found in correlation
11 protocols using stata statistical software v 13
1
Evaluating Questionnaire Reliability
2
Prompt Parkinson's Medication Delivery
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Linear regression was used to explore the association of timeliness of delivery of l -dopa medication with length of stay. We defined late medication delivery as 30 min or more after scheduled delivery time or documented omission of the drug dose. Each drug administration was considered as one event and data from multiple time points for each individual's hospital admission were adjusted for using robust standard errors. This generated a measure of the association between an additional missed or omitted dose of medication and length of stay for each hospital admission period. Secondary analyses looked at: the association between timing of dopamine agonists and length of stay; the association between prescription of contraindicated dopamine antagonists with length of stay; the effect of weekends v weekdays on prompt delivery of PD medications. All analyses were adjusted for gender, age as a categorical variable and the number of recorded medical diagnoses. Sensitivity analyses were performed using a broader definition of late medication administration with a 60-min delay from scheduled time. We were unable to complete a stratified analysis by l -dopa dose frequency as 47% of the population has a personally designed drug regimen. All statistical analysis used Stata statistical software (v13, Texas).
3
Factors Associated with Chemsex Use
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Data were systematically cleaned for inconsistencies and analyses were carried out in Stata statistical software v.13. The category ‘unknown’ was used to denote where the respondent either self‐reported ‘don't know’ or the response was left blank. In order to explore factors associated with chemsex use, we stratified demographic and lifestyle factors by whether or not an individual self‐reported chemsex and tested for differences between the two groups using χ2 test (for categorical data) or Kruskal–Wallis test (for continuous data). To see the effect of injecting drug use, we further stratified those engaging in chemsex by whether an individual had self‐reported injecting chems in the previous 12 months or not and tested for differences between both groups using the same tests as for chemsex use (see earlier).
We used logistic regression to explore factors associated with chemsex use. Factors that were significantly associated with the use of chemsex (p < 0.05) in the univariate model were used to develop a multivariable logistic regression model.
A second multivariate model was developed using the data of those who indicated they were taking ART, ‘multivariate (ART only)’ so that we could investigate the associations between chemsex use and self‐reported missed ART doses.
We used logistic regression to explore factors associated with chemsex use. Factors that were significantly associated with the use of chemsex (p < 0.05) in the univariate model were used to develop a multivariable logistic regression model.
A second multivariate model was developed using the data of those who indicated they were taking ART, ‘multivariate (ART only)’ so that we could investigate the associations between chemsex use and self‐reported missed ART doses.
4
Comprehensive Statistical Analysis of Data
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Shapiro-Wilk test was used to test normality. Descriptive indices (mean and SD) were used to describe quantitative variables and count and percent were used to describe qualitative variables. Likelihood ratio χ2 test, one-way and repeated measure analysis of variance (ANOVA), and correlation analysis was utilised to analyse data. Data analysis was performed using Stata statistical software V.13 (StataCorp) at a significance level of 0.05.
5
Evaluating Diagnostic Accuracy Across Modalities
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We calculated the PPVs with 95% CIs according to the Wilson Score method.8 (link) We computed PPVs separately for subgroups of echocardiography (transthoracic vs transoesophageal echocardiography) and PCI (unspecified PCI vs PCI with stent implantation). For ICDs, we disaggregated the sample into patients receiving ICDs for primary versus secondary prophylaxis.
Analyses were stratified by age group (<60, 60–80, and >80 years), sex and calendar year (2010, 2011 and 2012). The patients were sampled using SAS, V.9.2 (SAS Institute, Cary, North Carolina, USA), while the analyses were performed using Microsoft Excel 2010 and Stata statistical software, V.13 (StataCorp LP). In accordance with Danish law, no approval from the Ethics Committee was required.
Analyses were stratified by age group (<60, 60–80, and >80 years), sex and calendar year (2010, 2011 and 2012). The patients were sampled using SAS, V.9.2 (SAS Institute, Cary, North Carolina, USA), while the analyses were performed using Microsoft Excel 2010 and Stata statistical software, V.13 (StataCorp LP). In accordance with Danish law, no approval from the Ethics Committee was required.
6
Meta-Analysis of Diagnostic Accuracy
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Data analyses were performed using Meta-DiSc statistical software version 1.4 and Stata statistical software (v.13.0; Stata Corp, College Station, TX, USA). The Spearman correlation coefficient of logarithm sensitivity and 1-specificity was calculated to detect the threshold effect [14 (link)]. A χ2-based Cochran's Q test and Higgins’ I2 statistics were used to assess the heterogeneity among studies. A value of P<0.1 and I2>50% was considered significant heterogeneity [15 (link)]. The bivariate binomial mixed model was employed to summarize the pooled sensitivity, specificity, positive likelihood ratio(PLR), negative likelihood ratio(NLR) and diagnostic odds ratio (DOR) with 95% confidence intervals (CIs) [16 (link)]. A summary receiver operating characteristic curve (SROC) was constructed based on pooled sensitivity and specificity of included studies [17 (link)]. The area under the SROC curve (AUC) represents an analytical summary of test performance [18 (link)]. In addition, subgroup analyses and meta-regression were performed to explore the potential heterogeneity among included studies. Finally, publication bias was investigated by using Deek’s funnel plot [19 (link)]. All P values were two sided.
7
Statistical Analysis of Experimental Data
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We reported the mean (M) and standard deviation (SD) values for normally distributed data with the Shapiro-Wilk test, and the median (Me) and interquartile range (IQR) values otherwise. Differences between the 2 groups were assessed using the independent samples t test. The Mann-Whitney U test was applied to Me and IQR. For intergroup comparison, the quantitative parameter values between the groups were assessed with the Wilcoxon test. The χ 2 test and Fisher's test were used for rate comparison. The odds ratios (ORs) and 95% confidence intervals (CIs) were also reported. Statistical signifi cance was defined at p < 0.05. We performed statistical analyses using the Stata statistical software, v. 13.0 (StataCorp, College Station, USA).
8
Trauma Mortality Risk Factors
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Summary statistics are presented as median (25th-75th percentile) for continuous data and as frequency (percentage) for categorical data. The associations between patient characteristics and mortality for trauma cases recorded since January 2012 were investigated using binary logistic regression models. First, univariable models were constructed, and then multivariable models. In all multivariable models age, year of trauma, and ISS were included as covariables. The results of multivariable models are only presented when there are at least five deaths in each category, in order to avoid overfitting of the model. Effect estimates are presented as odds ratios (OR) and 95% confidence intervals (95%CI). Data analysis was undertaken using Stata statistical software v13.0 (StataCorp, College Station, TX, USA).
9
Analyzing Claims Against Military Health System
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The distribution of CC was analyzed and stratified according to the outcome of the CC (SC, UC, or CPD) and the associated surgical setting (“general surgery” [GS], OST, and GO) and classified according to the following aspects: year of claim against the health system; causes, consequences, and location of the triggering incident; related surgical specialty; and the existence of a second involved health center.
Qualitative variables were compared using the χ2 parametric test; and in the case of noncompliance with the application scenario for this parametric test, we used Fisher exact test. We compared the quantitative data (median times and costs) with polyatomic qualitative variables using the nonparametric Kruskal-Wallis test; and we used the Mann-Whitney U test for the comparison with dichotomous variables. We did a simple linear regression between the cost of the compensation of the SC and the year this was imposed on the MHS. Confidence intervals at 95% (α = 0.05) and significant P value for frequency estimates were estimated. Differences with P values less than 0.05 were considered statistically significant. The statistical exploitation of the data was carried out using the Stata v.13 statistical software.23
Qualitative variables were compared using the χ2 parametric test; and in the case of noncompliance with the application scenario for this parametric test, we used Fisher exact test. We compared the quantitative data (median times and costs) with polyatomic qualitative variables using the nonparametric Kruskal-Wallis test; and we used the Mann-Whitney U test for the comparison with dichotomous variables. We did a simple linear regression between the cost of the compensation of the SC and the year this was imposed on the MHS. Confidence intervals at 95% (α = 0.05) and significant P value for frequency estimates were estimated. Differences with P values less than 0.05 were considered statistically significant. The statistical exploitation of the data was carried out using the Stata v.13 statistical software.23
10
Nurses' Perceptions of NCS Tool
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A sample size of 543 respondents (±6%) was required to estimate the correct proportion of professional nurses’ perceptions of the NCS tool. This number provided a 95% probability of achieving the study’s objectives and assumed that 50% would yield a clear picture of their perceptions of the tool. The sample size was computed by using the Stata V13 statistical software. The command was power one proportion. Figure 1 shows the sample calculated using Stata V13 statistical software.
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