R version 4

All statistical analyses were performed using R version 4.1.2 and SPSS 24.0. The expression levels of the RNA methylation regulators were compared in DN samples versus controls using Wilcoxon rank-sum test. The normal distribution data was statistically analyzed by Student's-t test between two groups and more than two groups were performed by one-way ANOVA. Univariate logistic regression analyses were performed to determine the independent prognostic factors. P < 0.05 was considered has a statistically significant.

The Student t-test was used for continuous variables, while the classification variables were analyzed using the χ² test. Cox and LASSO regression models were used to analyze the predictors of RFS. The data were expressed as mean ± standard deviation. All data were analyzed with R version 4.1.2, SPSS 24.0 and GraphPad Prism 8.0. A P value <0.05 indicated a significant difference. All tests were repeated three times.

For continuous variables, mean ± standard deviation was used if they conformed to the normal distribution; otherwise, they were presented as median (quartile 25%, 75%). The comparison between the two groups for continuous data involved the t-test for normally distributed data and a non-parametric test for non-normally distributed data. Categorical variables were compared using Fisher’s exact test or the chi-square test. PFS, OS, and EMS were assessed using the Kaplan-Meier method with a log-rank test. The association between clinicopathological variables and PFS, OS, and EMS was assessed using univariate Cox proportional hazards regression analysis. Multivariate Cox proportional hazard regression analyses were performed to assess the relationship between Huaier granules and prognostic outcomes. R version 4.1.2 and SPSS version 26.0 (SPSS, Chicago, IL) were used for statistical analyses, with a significance level set at p < 0.05.

Survey data were collected, stored, and managed in Research Electronic Data Capture Version 10.0.1 [18 (link)] hosted at the University of New South Wales. Descriptive statistics were computed for all items. Respondents were grouped for analysis using medical field of practice (genetic HCP; reproductive care HCP; general practitioner; and hearing support HCP). Statistical analysis was performed using IBM Statistical Package for the Social Sciences software (SPSS version 23.0, SPSS Inc., Chicago, IL) and R, version 4.1.2 [19 ]. Categorical data were reported as frequencies and percentages with pairwise differences from an ordinal logistic regression were calculated. P-values for pairwise comparisons in the regressions were adjusted for multiple comparisons using Tukey’s method. Statistical significance was assessed at p < 0.05.
Respondents were asked one open-ended question on the topic, and the free text answers were separated according to professional group in Excel (Microsoft). Thematic analysis [20 (link)] was used to interpret the free text comments and identify themes relating to the inclusion of NSHL in RGCS. Coding and analysis were checked by LF, MD, and EK until consensus was reached.

All data were analyzed with IBM SPSS Statistics for Windows, version 26.0, (IBM Corp., Armonk, N.Y., USA) and R version 4.0.0 (Vienna, Austria; www.r-project.org/). The results of the concordant and discordant groups were compared using the Mann–Whitney U test for continuous numerical data, whereas the χ² test was used for binary categorical data. Data is presented as mean ± standard deviation for continuous variables with normal distributions and numbers (percentages) for binary categorical data. A p value of < 0.05 was considered to be statistically significant. Logistic regression analyses were conducted to identify independent variables predictive of maternal complications and neonatal outcomes among concordant and discordant twins. ORs and 95% confidence intervals (CIs) were calculated.

All data were analyzed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.0.0 (Vienna, Austria; www.r-project.org (accessed on 24 April 2020)). Data were compared using the Mann–Whitney U-test for continuous numerical data and the chi-square test for binary categorical data. After performing a normality test, the mean ± standard deviation was used to present continuous variables with normal distributions, whereas numbers (percentage) were used for binary categorical data. Variables with a p-value < 0.05 based on the Mann–Whitney U-test or chi-square test were used. A logistic regression analysis was conducted to identify the independent variables predictive of developmental delay according to chorionicity. Odds ratios and 95% confidence intervals (CIs) were calculated, and p-values of <0.05 were considered statistically significant.

The frequencies, percentages, position, and dispersion measures, depending on the type of variable, allowed us to present a descriptive analysis of the data. Then the Student’s t-test for independent samples and the chi-squared association test were used to contrast differences and/or discover non-existent relationships between the different variables, related to beliefs about the outbreak and about information provided on the pandemic, and also regarding the presence or not of psychological distress. The complete sample was categorized as non-health workers and those who were working away or from home.
The classification and regression trees (CART) method was used to design a binary tree with sample cases. Optimal cut-off points were selected to improve overall purity by minimizing the adjustment statistical value. Thus, cases in each part were similar within that part, and different from cases of any other part. Terminal nodes showed the predominant class, the proportion of psychological distress cases within the node, and the percentage of node cases over the sample total. The model allows the prediction of the percentage of those suffering psychological distress in new cases.
The analyses were carried out with the statistical software SPSS 26.0 (SPSS Inc., Chicago, IL, USA) and R version 4.0.0 (IBM, Armonk, NY, USA).