Statistical Analysis
Data are shown as mean±standard deviation for continuous variables or as number (%) for categorical variables. To identify distinct baseline to 5-year FTR risk trajectories, we used group-based trajectory models in a Stata plugin program (Stata Proc Traj). It identifes individuals’ clusters following a similar underlying trajectory on the dependent variable over time within a population, based on a maximum likelihood method. We developed different models by varying numbers of groups, ranging from two to five groups, and shapes (linear, quadratic, and cubic). We then compared them using Bayesian Information Criteria (BIC) and a sufficient proportion of participants in each subgroup. To ensure the adequacy of the selected model, we assessed four models that fit diagnostic criteria as suggested by Nagin: (1) average posterior probability of assignment for each group (AvePP) equal to 0.7 or greater for all groups; (2) the odds of correct classification (OCC) equal to 5 or higher for all groups; (3) similarity between the proportion of a sample assigned to a specific group and the group probabilities estimated from the model; and (4) narrow CIs of the estimated proportion. After identifying FTR longitudinal trajectory groups, we evaluated the associations of trajectory subgroup membership (as a categorical exposure) with incident RVD after the five examination cycle using logistic regression model. Statistical significance was considered using a two-sided P <0.05. All analyses were performed using SPSS 26.0 statistical software and STATA 14.2 statistical software.
Results