Between-group differences for continuous and categorical variables were evaluated by the Student’s t-test and Chi-square test, respectively. A Kaplan-Meier analysis plotted the cumulative survival rates between the matched study groups.
The mortality risks in the 2 cohorts were identified using hazard ratios (HRs) and 95% confidence intervals (CIs). We applied multivariable Cox proportional hazard models and adjusted for covariates such as diagnostic age, molecular phenotype, and CHM use to evaluate the survival of breast cancer patients. For the herbal prescription analysis, we divided all CHM into 2 subgroups: single herb or herbal formula. The Chi-square test was used to evaluate the association between these subgroups and the survival rate among breast cancer patients. All statistical analyses were performed using the SAS System for Windows statistical software, version 9.4 (SAS Institute Inc., Cary, NC, USA) and R software (version 3.6.1). The significant criterion was set at less than 0.05 for 2-sided testing of a P-value.
The mortality risks in the 2 cohorts were identified using hazard ratios (HRs) and 95% confidence intervals (CIs). We applied multivariable Cox proportional hazard models and adjusted for covariates such as diagnostic age, molecular phenotype, and CHM use to evaluate the survival of breast cancer patients. For the herbal prescription analysis, we divided all CHM into 2 subgroups: single herb or herbal formula. The Chi-square test was used to evaluate the association between these subgroups and the survival rate among breast cancer patients. All statistical analyses were performed using the SAS System for Windows statistical software, version 9.4 (SAS Institute Inc., Cary, NC, USA) and R software (version 3.6.1). The significant criterion was set at less than 0.05 for 2-sided testing of a P-value.