In the analyzed study, we conducted an inverse-variance weighted (IVW) meta-analysis as the primary analysis method, employing a random-effects model (18 (link)). Additionally, we performed two sensitivity analyses using different methods. The first sensitivity analysis involved the weighted median method, which provides valid estimates if at least 50% of the information came from valid instrumental variables (19 (link), 20 (link)). The second sensitivity analysis employed the MR-Egger method to evaluate the presence of horizontal pleiotropy in the selected instrumental variables (21 (link), 22 (link)). To assess heterogeneity among the instrumental variables, we used Cochran's Q-value. Furthermore, we performed a leave-one-out sensitivity analysis to examine the potential impact of individual single nucleotide polymorphisms. The analytical procedures were carried out using R (version 4.3.1) and R Studio (version 2022.06.1 + 524). We considered statistical significance to be defined as P < 0.05.
R version 4
Manufactured by Posit
Sourced in United States, Austria
R version 4.0.3 is a programming language and software environment for statistical computing and graphics. It is a free and open-source software project that provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.
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Lab products found in correlation
Rstudio version 1, by Posit (13 mentions)
Rstudio interface version 1, by Posit (4 mentions)
Sas version 9, by SAS Institute (4 mentions)
Rstudio 2022, by Posit (2 mentions)
Rstudio software, by Posit (2 mentions)
Sas 9.4 version, by SAS Institute (2 mentions)
Prism 9, by GraphPad (1 mentions)
R studio v1, by Posit (1 mentions)
Rstudio version 4, by Posit (1 mentions)
Rstudio ide, by Posit (1 mentions)
Rstudio version 2022.07.1, by Posit (1 mentions)
Rstudio interface, by Posit (1 mentions)
Matlab r2018b, by MathWorks (1 mentions)
Prism 8, by GraphPad (1 mentions)
Rstudio team, by Posit (1 mentions)
Prism software, by GraphPad (1 mentions)
Spss statistics 26, by IBM (1 mentions)
Rstudio version, by Posit (1 mentions)
Prism for macos, by GraphPad (1 mentions)
110 protocols using r version 4
1
Mendelian Randomization Sensitivity Analyses
2
Risk Factors for In-Hospital Mortality
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Descriptive statistics were used to summarize patient demographics and clinical characteristics. Data are presented as percentages, means and standard deviations (SD), and medians and interquartile ranges (IQR), as appropriate. Comparisons between groups were made by Fisher’s exact test or Wilcoxon rank sum test, as appropriate. Univariate time-to-event analysis was performed by Kaplan–Meier survival estimates and log-rank tests.
Univariate and multivariate logistic regression analyses for potential risk factors associated with all-cause in-hospital mortality were performed. Variables with a p-value < 0.10 in univariate analysis and those with biological plausibility were included in the multivariate model. A two-sided p-value ≤ 0.05 was considered statistically significant for all comparisons. All statistical analyses were performed using R version 4.3.1 and RStudio version 2023.03.2.
Univariate and multivariate logistic regression analyses for potential risk factors associated with all-cause in-hospital mortality were performed. Variables with a p-value < 0.10 in univariate analysis and those with biological plausibility were included in the multivariate model. A two-sided p-value ≤ 0.05 was considered statistically significant for all comparisons. All statistical analyses were performed using R version 4.3.1 and RStudio version 2023.03.2.
3
Epidemiological Analysis of COVID-19 Outcomes
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Continuous and ordinal variables were described by the mean and standard deviation or median and interquartile range as appropriate. Categorical variables were described as counts and percentage. For all comparisons between groups, the χ² test, or the Fisher exact test whenever necessary, were applied for categorical variables. Continuous variables were tested for normality with the Kolmogorov-Smirnov test and then compared between groups using a t test, Mann-Whitney test, ANOVA, Kruskal-Wallis test when appropriate. All tests were performed as 2-sided and P values <0.05 were considered significant. To adjust for potential confounding variables, we performed a linear regression analysis adjusting for severity (measured in the WHO scale) and age. We performed a correction of the p values according to the method described by Benjamini and Hochberg (BH) controlling for the false discovery rate. The statistical analysis was performed in R version 4.3.1 and RStudio.
4
Examining COVID-19 Impacts on Well-being
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All analyses were carried out using R (Version 4.3.1) and RStudio. The descriptive analysis was presented as the mean, and standard deviation for continuous variables, whereas the categorical variables were presented as frequency and percentages.
The bivariate and multivariable linear regression models with 95% confidence intervals (CIs) were applied to investigate the association between factors and well-being. We checked correlations among the independent variables by Spearman correlation to avoid multicollinearity. If an independent variable correlated with one another at rho ≥ 0.3, one representative variable was selected for the multivariate models. A p-value of < 0.05 was used as an indicator of statistical significance.
We developed the conceptual framework based on our literature review, which included the association between demographic variables, DHL, information satisfaction, the importance of online searching for information related to COVID-19, fear of COVID-19, and well-being .17 (link),20 ,29 ,37 (link) The structural equation model (SEM) was utilized to analyze the indirect effects, direct effects, and total effects of mediators on the association between DHL and well-being. Lavaan package in R was employed to establish the SEM and conducted pathway analysis.38
The bivariate and multivariable linear regression models with 95% confidence intervals (CIs) were applied to investigate the association between factors and well-being. We checked correlations among the independent variables by Spearman correlation to avoid multicollinearity. If an independent variable correlated with one another at rho ≥ 0.3, one representative variable was selected for the multivariate models. A p-value of < 0.05 was used as an indicator of statistical significance.
We developed the conceptual framework based on our literature review, which included the association between demographic variables, DHL, information satisfaction, the importance of online searching for information related to COVID-19, fear of COVID-19, and well-being .17 (link),20 ,29 ,37 (link) The structural equation model (SEM) was utilized to analyze the indirect effects, direct effects, and total effects of mediators on the association between DHL and well-being. Lavaan package in R was employed to establish the SEM and conducted pathway analysis.38
5
Sensitivity Analysis of Lipid-GDM Link
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To ensure the reliability of the identified causal effect of lipids and apolipoproteins on GDM, we carried out a thorough set of sensitivity analyses. Cochran’s Q statistic was utilised to assess potential heterogeneity within the data [22 (link)]. The MR-Egger intercept analysis was employed to investigate horizontal pleiotropy [23 (link)]. We also conducted a Leave-one-out analysis to examine if any single SNP substantially affected the outcomes by systematically removing SNPs individually. Additionally, reverse MR analyses were performed to explore the potential reverse causal link between lipids and apolipoproteins (as seen in the forward MR analysis) and GDM.
For multivariable MR analysis, we applied two models to further understand the connection between lipid-related traits and GDM risk. In Model 1, five lipid-related traits (apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides) were included in multivariable analysis.
In Model 2, we included BMI for analysis, along with the three traits that showed positive associations in univariable analysis: apoA-I, HDL cholesterol, and triglycerides.
All analyses were performed using R (version 4.2.0) and RStudio, employing the R packages “TwoSampleMR” and “MR-PRESSO”.
For multivariable MR analysis, we applied two models to further understand the connection between lipid-related traits and GDM risk. In Model 1, five lipid-related traits (apoA-I, apoB, LDL cholesterol, HDL cholesterol, and triglycerides) were included in multivariable analysis.
In Model 2, we included BMI for analysis, along with the three traits that showed positive associations in univariable analysis: apoA-I, HDL cholesterol, and triglycerides.
All analyses were performed using R (version 4.2.0) and RStudio, employing the R packages “TwoSampleMR” and “MR-PRESSO”.
6
Spatiotemporal Analysis of Opioid Crisis
7
Comprehensive Statistical Analysis Workflow
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Statistical analysis was performed using R version 4.2.0 (2022-04-22) and RStudio (version 2022.02.3 + 492; RStudio, Inc.). Demographic data were summarized descriptively using frequency counts and percentages of the total study population for categorical variables. For continuous variables, the median with the interquartile range (IQR) were used, based on the distribution of data tested by visualization with histograms and quantile-quantile plots, as well as normality testing using the Shapiro–Wilk test. Sample sizes are indicated (n) for each analysis.
8
Demographic and Clinical Factors Associated with CAD
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Descriptive statistics were used for baseline demographic and clinical characteristics. According to the data distribution, continuous variables are presented as medians and interquartile ranges (IQR). Categorical variables are described with frequencies and percentages. Baseline characteristics were compared across age (<50 years and ≥50 years) using X2 and Mann-Whitney U tests for qualitative and quantitative variables, respectively. To avoid the effect of group sizes on hypothesis tests, we also estimated absolute standardized differences (ASD), and values ≥ 10% were considered significant [21 (link)].
To identify the characteristics associated with CAD, we used a logistic regression model. Variables retrieved in the final model were selected following a combination of the statistical criterion and their clinical relevance. We also repeated the final model by age (<50 years and ≥50 years) as a sensitivity analysis to evaluate a potential effect modification. Effect measures were odds ratios (OR) and their 95% confidence intervals. A complete-case approach was used. Hosmer and Lemeshow's test evaluated the goodness of fit in this model. Statistical significance was set at a p-value ≤0.05. R version 4.2.0 [22 ] and RStudio [23 ] were used for statistical analyses.
To identify the characteristics associated with CAD, we used a logistic regression model. Variables retrieved in the final model were selected following a combination of the statistical criterion and their clinical relevance. We also repeated the final model by age (<50 years and ≥50 years) as a sensitivity analysis to evaluate a potential effect modification. Effect measures were odds ratios (OR) and their 95% confidence intervals. A complete-case approach was used. Hosmer and Lemeshow's test evaluated the goodness of fit in this model. Statistical significance was set at a p-value ≤0.05. R version 4.2.0 [22 ] and RStudio [23 ] were used for statistical analyses.
9
Non-Parametric Statistical Analysis
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Statistical analyses were performed in R version 4.2.0 with RStudio 2022.2 and GraphPad 9.2.0 and details are given in supplementary methods. Each dataset was first tested for normality using the Shapiro–Wilk test. Because data were not normally distributed, the nonparametric Wilcoxon rank sum test with false-discovery rate correction was used. P values <0.05 were considered statistically significant.
10
Sociodemographics and Resource Utilization
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Descriptive statistics summarized sociodemographics and resource use. Continuous variables were presented as median ± standard deviation (SD), while categorical variables were presented as frequencies and percentages. Analyses were conducted using R (version 4.2.0) in RStudio.
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