Statistical Analysis
REGRESSION
We used Mplus 8.0 to apply the GMM on HAMD scores to identify multiple trajectory subgroups. The unconditional model was fitted (without covariates), starting with a single-class model and successively increasing the number of classes. The best-fitting model was selected according to the following criteria: (1) lowest Akaike information criterion, Bayesian information criterion (BIC), and Sample-Size Adjusted BIC; (2) higher entropy and latent class probability value (>.90); (3) After the GMM procedure, the demographic characteristics and baseline clinical scores were analyzed according to the optimal classes. The categorical variables were examined by chi-square analysis. Due to the differences in the sample size between the 3 groups, the non-parametric Kruskal-Wallis test was applied. Then, logistic regression analysis was conducted to explore the putative predictors of the classes. To avoid the overfitting of the regression model, the number of covariates should be no more than 2. The area under the curve (AUC) of the receiver operating characteristic curve was calculated to measure the model discrimination. We applied automated forward stepwise selection with variable elimination when 