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