R statistical software

Statistical analysis was done using STATA (version 15; STATACorp, College Station, TX, USA), R Statistical Software (version 3.4.3; https://www.r-project.org/), and NONMEM software (version 7.4; Icon Development Solutions, Ellicott City, MD). Data visualization was performed using R Statistical Software (version 3.4.3; https://www.r-project.org/). Age, weight, and height measurements were used to calculate weight-for-age z-score (WAZ), height-for-age z-score (HAZ), and weight-for-height z-score (WHZ) using the WHO Anthro macro for STATA (46 – 48 ). A child was considered underweight, stunted, or wasted if the child had a WAZ of <−2, a HAZ of <−2, or a WHZ of <−2, respectively. CFZ and BDQ doses were summarized as weekly dose to normalize between children requiring different dosing frequencies according to weight. For summary of adverse events, children who received LFX alone were used as the reference since LFX-induced QT prolongation is minimal (49 (link)– (link)52 (link)).

The emotional responses were analyzed using R statistical software (version 3.5.1) (https://www.r-project.org/). In accordance with the first hypothesis, two analyses were conducted wherein both groups were compared for emotional labels and intensity of ratings.
For all analyses, a mean data matrix of dimensions 24 (raga excerpts) X 8 (emotions) were created separately for the two cultures. Each matrix entry was an average of all participant ratings of the group for that particular excerpt and emotion, thus serving as an average of individual rating values.

All statistical analyses were conducted using GraphPad Prism v8.0 (GraphPad Software) or R Statistical Software (v4.1 for Windows). The statistical tests are indicated in the Figure legends.

To compare and assess the diagnostic performance of both QFR and the QFR–FFR hybrid approach, the clinical standard of FFR ≤ 0.80 indicating hemodynamically significant stenosis was applied. The same threshold of ≤ 0.80 was applied for the QFR measurements. For the statistical analyses, the distribution of continuous variables was assessed using histograms and Q–Q plots. Continuous variables were expressed as means and standard deviations (SD) if normally distributed or medians and 25–75% interquartile ranges (IQR) if non-normally distributed. Categorical variables were represented as totals and percentages. Diagnostic performance on a per vessel-basis as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated. The variables were calculated as proportions with a 95% confidence interval. The correlation, differences, and diagnostic performance between QFR and wire-based FFR were further assessed using Pearson correlation coefficient, Bland–Altman plots, receiver operating characteristics-curves (ROC-curves) and area under the ROC curve (AUROC-curve). QFR–FFR hybrid approaches using various cut-off points for the intermediate zone in which FFR is measured were simulated. All statistical analyses were performed using R statistical software (www.r-project.org, version 3.6.2).

Our primary outcomes of interest were the noise corrected values of total spatial heterogeneity of perfusion (CV²_Qtotal) and its components, including the heterogeneity generated by the vertical gradient in perfusion (CV²_Qvgrad) and the residual perfusion heterogeneity (CV²_Qr) and its length scale components. In secondary analyses, we assessed various regional perfusion and ventilation metrics. We performed two-sided T-tests to evaluate whether there was a difference in means of each metric between study groups and for baseline vs. O₂ + iNO within study groups. Additionally, we obtained Cohen’s d effect size estimates for the primary parameters. All analyses were performed using R Statistical Software (v4.1.3) [30 ]. Effect sizes were estimated using the effsize R package (v0.8.1) [31 ]. Statistical significance was set at p < 0.05. Data are presented as mean (range) unless otherwise stated.

For each selected clinical trial, the number of subjects who developed the AEs and SAEs, as well as the total number of subjects at risk in the placebo arm or the standard of care arm were extracted. For each anticipated event, incidence rates were calculated using the number of patients with the anticipated events and the total number of the subjects at risk. The frequency and the 95% confidence intervals (CI) were calculated using Clopper–Pearson method for a single proportion. For the AE and SAE identified in more than one trial, we performed a meta-analysis with a random effects model using R statistical software (https://www.R-project.org/) and the “DescTools”, “meta” and “metafor” packages to obtain a summary estimate and 95% confidence interval.