Stata se software

Correlations between variables were assessed by Spearman’s rank coefficient, with Benjamini-Hochberg correction for multiple comparisons. Continuous data were compared between matched case/control pairs by Sign-test and between non-paired groups by Mann-Whitney test. BoR was compared between groups by a Test for trend across ordered groups and between isolates transcribing var/DCs at low- or high-levels by negative binomial regression models adjusted by age. Mean ratio of IgGs and 95% confidence intervals between Mozambican children and Spanish adults, as well as between Mozambican children older than 2.5 years of age and less than 2.5 years were calculated in linear regression models, with log-transformed MFIs. Statistical analysis was performed with Stata/SE software (version 12.0; StataCorp).

Descriptive statistics were presented as frequencies, means and standard deviations, or medians and interquartile ranges (IQRs), as appropriate. The Kolmogorov-Smirnov test was used to examine the normality of continuous variables, which were then compared using a Student's t-test or Mann-Whitney U-test, as appropriate. Categorical data were analyzed using a chi-squared test or Fisher's exact test. All tests were two-tailed, and a p-value < 0.05 was considered to indicate a statistically significant difference.
Factors potentially associated with newly-detected AF were evaluated using descriptive statistics. Univariable logistic regression models were used to evaluate candidate variables. Odds ratios (ORs) together with 95% confidence intervals (CIs) were reported, and p-values < 0.05 were considered to indicate a statistically significant difference. We built a multivariable regression model based on all potential predictors using forward stepwise selection with p < 0.05. Then the “nomolog” package was used to establish a predictive model and generate the nomogram to predict newly detected AF. We also examined discrimination using the C-statistic in our regression model, CHA2DS2-VASc, CHARGE-AF, and EHR-AF scores. Analyses were performed using Stata SE software (version 15.1; StataCorp, College Station, TX).

A random effects model was used for pooling data for survival. Interstudy heterogeneity was assessed with Cochran’s Q-test. The percentage of total variation across studies owing to heterogeneity was evaluated by the I² measure. Owing to differences in defining and reporting adverse events (AEs) in the individual studies, pooling the data may have been misleading; therefore, safety was evaluated qualitatively. Pooled survival was estimated using StataSE software (StataCorp, College Station, TX).