Statistical analysis
depressive, RS, psychiatric
REGRESSION, SECONDARY
Required sample sizes for adequate power were calculated using the G*Power program. Based on a one-sided α of 0.05, a sample size of 102 evaluable patients in each treatment group was needed to identify the effect size of 0.35 at 80% power. Assuming a dropout rate of 10%, a minimum sample of 204 evaluable patients (102 patients in each group) was needed.Data missing for patients who dropped out were imputed via the last observation carried forward (LOCF) method. All analyses to evaluate between-group differences were conducted for an intent to treat (ITT) analysis set that included all the patients who received treatment. Demographic characteristics, baseline psychiatric symptoms, depressive symptoms, and psychosocial functioning between the RS and control groups were analyzed by one-way analysis of variance (ANOVA).A repeated measures multivariate analysis of variance (RM-MANOVA) was used to compare the efficacy of the two treatment regimens. For dependent variables, prolactin levels and symptom scores measured at 5 time points were considered as repeated measures within effects, treatment groups (RS and control groups) were considered as between effects, and age, gender, age at onset, and education were covariates. In each RM-ANOVA model, the independent variables were outcome measures over time. If significant differences were observed in the interaction effect of the treatment group by time, an analysis of covariance (ANCOVA) with baseline value as covariates were conducted to examine differences in symptom scores assessed at different time points (months 1, 2, 3, and 6). In addition, ANCOVA was performed to compare the changes in symptom scores from baseline to follow-up.Linear regression analysis was performed to identify variables associated with improvement in primary or secondary outcome measures. The following variables were potentially associated with the outcome measure and were therefore added to the regression model: baseline symptoms, gender, age, age at onset, duration of illness, and education.Data were analyzed using PASW Statistics (SPSS 22.0, Inc., Chicago). Significance was set at p < 0.05 for all analyses. Given the multiple comparisons in the multivariate analysis, Bonferroni’s correction was used.