Statistical analysis
exacerbations
REGRESSION
All statistical analyses were performed in R (version 3.6.0, 2020). P-values were considered significant at two-tailed 0.05 level. Descriptive statistics were compared using Pearson Chi-square test for categorical variables and Kruskal–Wallis test for continuous variables.To construct the treatment benefit prediction model for the primary endpoint (exacerbation rate reduction), we fitted a generalized linear model (GLM) with Poisson distribution and logarithmic link function, with the number of severe exacerbations during follow-up as the dependent variable and the logarithm of total follow-up days as the offset. The covariates and their first-order interaction with treatment were the independent variables. To prevent overfitting, we applied Lasso regression to estimate model parameters, which shrank the coefficients of non-essential covariates to zero [We predicted treatment response for each individual, based on the fitted GLM models, as the difference in the predicted rate of exacerbation or the ACQ5 score between treatment and no treatment [To evaluate predictive performance for both endpoints, we assessed model calibration based on calibration plots and the root mean squared error, with details provided in The Lasso-selected predictors were ranked based on their importance in predicting treatment response. Importance was quantified as the absolute value of magnitudes of coefficients in a linear regression model with predicted treatment response as the dependent variable and standardized predictors as independent variables.