Spss statistics version 25.0 for windows

Continuous variables were examined for distribution. Normally distributed data were analyzed using an independent t test, whereas non-parametric data were analyzed with a non-parametric Mann-Whitney U test. Categorical variables were compared between groups using a χ² test (Fisher’s exact test when needed). To assess the association between predictors and outcome, we first compared variables in univariate analysis. We considered p values < 0.05 as statistically significant. Significant not treatment-related variables that associated with outcome in the univariate analysis were included in a multivariable logistic regression model to find independent predictors associated with outcome. To avoid collinearity, we included WFNS grade instead of GCS score as WFNS grade is established in the SAH literature and allows for dichotomization into poor (WFNS IV to V) and good grade (WFNS I to III) [8 (link)]. We did not include variables from the univariate analysis with a high number of missing values. Age was included in all multivariable analyses. We separately analyzed patients with PMH and nPMH. SPSS Statistics version 25.0 for Windows (IBM Corp, Armonk, NY, USA) was used for all statistical analyses.

The baseline characteristics were compared between the ganciclovir-treated and non-treated groups, using Mann-Whitney U test for continuous variables and Fisher’s exact tests for categorical variables. We used Cox proportional hazard regression analysis to evaluate the association between ganciclovir treatment and long-term prognosis. In the multivariate-adjusted model, we included ICU stays, qSOFA score, CWIs, age, CMV PCR titer more than 5000 copies/ml, and variables with statistical significance in the univariate analysis were included in the multivariate analysis. The same statistical analyses were conducted for the comparison between CMV-positive and negative patients. For all analyses, a two-tailed p value < 0.05 was considered statistically significant. IBM SPSS Statistics version 25.0 for Windows (IBM, Armonk, NY, USA) was used for all statistical analyses.

We conducted two-sample MR analyses to examine the potential causal association between HRV and AF. The inverse variance weighted (IVW) method is the main method used in our analyses [29 (link)]. MR estimates were presented as odds ratios (ORs) with corresponding 95% CIs (Fig. 1). See the Methods S3, for more details on the rationale, assumptions and sensitivity analyses of the MR analyses [15 (link), 29 (link)–35 (link)].
A two-tailed p < 0.05 was considered statistically significant. The data management was done using IBM SPSS Statistics version 25.0 for Windows (IBM Corp, Armonk, New York). The statistical analyses were done using the R packages “JMbayes2”, [36 ] and “TwoSampleMR”(30, 34, 35) in R software (R 4.0.2; R Foundation for Statistical Computing, Vienna, Austria) [37 ].

Data analysis was performed using IBM SPSS Statistics version 25.0 for Windows (Armonk, NY, USA: IBM Corp.). Descriptive and inferential statistics methodologies were applied. The Chi-square test was used to assess the association between relevant variables. A level of significance of 5% was set for all inferential analyses.