Stata software version 16

Quantitative variables were measures of central tendency type average (x̄ or Median [Me]) with their respective measures of dispersion standard deviation (SD) and interquartile range (IQR) depending on the normality of their distribution, which was calculated using the Shapiro–Wilk test. The descriptive analysis of qualitative variables was performed by calculating absolute and relative frequencies. For the comparison of the distribution of postoperative complications for clinical, surgical procedure‐related and postoperative variables, the chi‐square test or the Mann Whitney test was used for qualitative variables, depending on the result of the normality test; for the comparison of distributions of quantitative variables, the ANOVA test or the Kruskall–Wallis test was used, depending on the distribution of the variable.
In addition, a Poisson regression analysis was performed to determine the prevalence ratios (PR) associated with the significant variables in the bivariate analysis, with the presence of postoperative complications as the dependent variable. An analysis was performed controlling for possible confounding variables. Values of p < 0.05 were taken as significant. Data analysis was performed with the Stata software version 16.0 (StataCorp, Texas, USA).

We summarized the baseline characteristics of included trials by using standard descriptive statistics separately for industry- or non–industry-sponsored trials. We then assessed the percentage of trials in each of the status, duration, and recruitment categories described herein and reported discrepancies between planned and actual trial conduct by industry sponsorship status. Absolute and relative differences between anticipated and actual trial duration are presented as medians (IQRs) for trials completed with delays and discontinued trials. Similarly, differences between anticipated and actual recruitment are presented for discontinued trials and those completed without meeting their recruitment target. To determine whether industry sponsorship was associated with trials being completed on time or with full enrollment, we conducted χ² tests with a 2-sided P < .05 used to indicate statistical significance. Data were prepared and analyzed between October 27, 2021, and June 30, 2022, using Stata software, version 16.0 (StataCorp LLC) and R software, version 4.1.2 (R Foundation for Statistical Computing).

We used one-sample K-S test to examine the normality of obtained data. Descriptive analyses about the sample’s socio-demographic, PA and sedentary behavior times, happiness level, depression, anxiety, stress status, and other related factors were conducted. Demographic, PA, happiness, and related variables of male and female students were compared using t-test or Mann-Whitney U-test for continuous variables and the chi-squared test for categorical variables. We used backward stepwise multiple regression analysis using subjective happiness level as the dependent variable to assess the effects of variables including PA, sedentary behavior, demographic, and other related factors in male and female students.
All statistical analyses were performed through the STATA software version 16.0 (Stata Corporation, College Station, TX, United States), with the significance level at the p-value of 0.05 (two-tailed).

All data analyses in this study were performed in STATA^® software version 16.0 and the results are presented in tables.
The characterization of the municipalities and the expenses with hospital admissions for stroke was performed using descriptive statistics procedures (frequencies, means, and standard deviations). To verify the differences between means, the Student’s t-test was used. The evaluation of the effects of the HGP on hospitalization expenses for stroke was performed using a PSM-DID estimation strategy in a Fixed Effect data model for multiple periods. The analytical procedures involve validation tests of the estimation model and the empirical strategy (pre-tests), estimation of the PSM-DID model, and validation of the results found with the estimations (post-estimation).

One-sample K-S test was used to detect the normality of obtained data. Descriptive analyses were used to describe the sample’s socio-demographic, burnout symptoms, and work-related factors.
Since there is no consensus on the diagnostic criteria for burnout syndromes, some suggested the three subscales should be treated as continuous measures (Rotenstein et al., 2018 (link)). We adopted this approach in our analysis. The Chi-square test examined the gender difference of burnout prevalence. The Kruskal–Wallis test or Pearson correlation analysis was conducted to test the correlation between related factors and EE, DP, and PA in male and female participants. After that, significant factors were involved in further regression analysis. As all endocrinologists nested in hospitals, multilevel linear regression analyses were conducted to identify independent factors associated with EE, DP, PA in male and female samples, respectively.
We performed all statistical analyses using the STATA software version 16.0 (Stata Corporation, College Station, TX, United States), with the significance level at the p-value of 0.05 (two-tailed).