Spss statistics software version 21

We assessed differences between ADHD and control subjects using t tests for continuous variables and chi-square tests for categorical outcomes. Pearson correlation coefficients were used to evaluate relationships between clinical variables.
Mediation models were developed to examine the influence of ADHD symptom severity on functional impairment. Separate model were estimated for each independent variable (subscales from CAARS) and dependent variable (SDS scores). Mediation models estimate the indirect effects of an independent variable (in this case, ADHD symptom severity) on a dependent variable (functional impairment) after accounting for a mediator (anxiety). We used 5,000 bootstrap resamples to provide stable estimates of direct, indirect, and total effects. Confidence intervals were established using the bias-corrected and accelerated method. Mediation was tested using the Hayes40) PROCESS modeling approach in model 4 of IBM SPSS Statistics software version 21.0 (IBM Co., Armonk, NY, USA). This analytic approach permitted the examination of both direct and indirect effects of ADHD symptom severity and anxiety on functional impairment in subjects with adult ADHD.
All tests were two-tailed, and alpha level was set at 0.05 for all analyses. We calculated all statistics using IBM SPSS Statistics software version 21.0.

Categorical variables were described as frequency rates and percentage, and continuous variables were described using mean±standard deviation (SD) and median (IQR). A normality test was performed using Kolmogorov–Smirnov test. Normally distributed data were analyzed using an independent t-test, while non-normally distributed data were analyzed using the Mann–Whitney U-test. Non-normally distributed data from repeated measures were also compared using the generalized linear mixed model. The area under the curve (AUC) and 95% confidence interval (CI) of the receiver operating characteristic (ROC) curve were used to assess the accuracy of each biomarker in predicting COVID-19 severity. The severity of COVID‐19 was predicted by optimal cut‐off points determined by Youden’s index. All data and statistical analysis were conducted using IBM SPSS Statistics software version 21.0 (IBM Corp., Armonk, NY, USA). Two-sided p-values <0.05 were considered to indicate statistically significant differences.