2.4 Statistical Analysis
The categorical and continuous variables were compared using the χ2 and Student’s t‐test, respectively. Wilcoxon’s rank-sum test was used for comparisons of lung density indices in CT scans with and without BO. Correlations between the various quantitative lung density indices and PFT parameters were compared using Spearman’s rho (ρ), due to the heteroscedastic nature of the data. We also tested the association between LDIs and PFTs in linear regression models, with adjustment for age and sex. In multivariate logistic regression, we analyzed the odds ratio (OR) of the diagnosis of BO by lung density and PFT parameters. Stepwise backward elimination and all subset regressions were performed to select the final model in multivariate analysis. The receiver operating characteristic (ROC) method was performed to evaluate the utility of different lung density parameters for predicting physician-diagnosed BO. Areas under the curve (AUCs) and optimal cut‐off points based on maximizing the sum of sensitivity and specificity were calculated for each densitometry parameter. P values of <0.05 were considered statistically significant; the statistical analyses were performed using R Version 4.0.5 (https://cran.r-project.org/web/packages/maxstat/index.html). This study was approved by the Institutional Review Board of Seoul St. Mary’s hospital.