DISCUSSION
fatigue, CRC, pain
In this study, we clustered patients separately for the experimental and control treatment arms of the CO.20 trial into several new patient classes through GMM. For the new patient classes or subgroups, each had a different longitudinal global QoL score trajectory that was associated with differences in their survival status and had different distributions by sex and performance status. Without GMM clustering, these differences might have been left undetected. When comparing the two treatment arms, there were no significant differences in baseline clinical characteristics and no differences in the outcome of OS between the two treatment arms; this provided a consistent baseline level for our analysis.We hypothesized that different classes of CRC patients would present with different QoL longitudinal trajectories over time and that these differences might affect their OS. To test this hypothesis, we utilized GMM to cluster the patients of each treatment arm in separate analyses. We included variables in our GMM analyses that might affect the longitudinal QoL. Therefore, a LMM helped us to obtain the clinical variables that were strongly correlated with QoL: sex and ECOG.Clustering is the unsupervised classification of objects (in this case, patients) into different groups.Our results demonstrated that GMM has satisfactory clustering ability for QoL data. After clustering CRC patients by their QoL data using GMM, each treatment arm was divided into three new classes. The proportion of each class in both treatment arms was larger than 10%. The entropies for the chosen models in the two treatment arms were higher than 0.6. Trajectories of estimated means of QoL for conditional GMM were significantly different for the classes of patients for the two treatment arms. To assess the association of QoL change and OS, we conducted landmark analysis by using the OS starting from 16 weeks and excluded the patients who died before 16 weeks. In addition to that, sensitivity tests were conducted by using the OS starting from 8 weeks and excluded the patients who died before 8 weeks. The results showed that there are still significant differences in survival curves among different classes, which further indicated that the stability of GMM was robust. Besides that, the identified subgroups based on the unsupervised learning show significant overall survival differences which infer the prognostic effect of longitudinal QoL measures during treatment.Collectively in this study, GMMs can separate CRC patients well into different classes for each of the treatment arms. In our study, the discrimination ability of gender, ECOG and GMM classes, as shown by our reported C‐indices suggest that the selected variables could classify different groups well, which further verified the accuracy of our model.We further compared the clinical characteristics of the new clusters and found that there were significant differences between gender and ECOG performance status in both the experimental and control treatment arms. In third classes (3E/3C) of each of the treatment arms, females had poorer QoL than males. A recent study showed that women reported more fatigue and pain than men for CRC patients.Finally, we found that the classes clustered by GMM are associated with differential OS, which is due to the different time‐related trajectories of QoL. Although clinicians understand that the higher the QoL, the better the OS, we need GMM to cluster the classes with potential heterogeneity. In addition, GMM outputted three parameters from the longitudinal QoL. They are all significantly correlated with survival performance.