Statistical analysis
CRC
REGRESSION
Fisher's exact test was used for comparison on categorical variables and Kruskal–Wallis was used for comparison on continuous variables. A linear mixed effect model (LMM) was used to screen significant variables to establish a GMM. We focused on clustering modeling approaches for longitudinal QoL study. Figure Framework of a linear GMM with predictors at four time points. C is the latent categorical variable; x is the covariate; For simplicity, in this study, we assumed that longitudinal QoL data within 16 weeks of observation window were missing at random. We used full information maximum likelihood (FIML) to estimate the model with missing data rather than either excluding missing data or using multiple imputations to generate values for the missing data. FIML is popularly used in Structural Equation Modeling (SEM), multi‐level model or growth models. The FIML estimator maximized the sum of the n‐case‐wise likelihood function with all available raw data.The method of Kaplan–Meier was used to analyze the landmark OS between different subgroups, starting from the last QoL time‐point (i.e., 16 weeks). Cox regression was applied to test if there was a significant association between baseline QoL, individual slope, and the quadratic term from the GMM output with overall survival for CRC patients. C index, or concordance index, was used to evaluate the prediction ability of the model. Concordance indices estimate the probability that the predicted results are consistent with the actual observed value.