analysis [51–52]. This approach to missing data is not imputation-based, but relies on estimating model parameters (e.g., individual trajectories) using all available data (e.g., existing measurement occasions for the individual, overall sample parameter estimates, model covariates), and is currently the standard for handling missing data in longitudinal studies [53–54]. Overall missing data was modest at 23% across the study duration, with the largest proportion of missing observations at week 50 follow-up (see Supplementary Table 1). Complete case (N = 419) sensitivity analyses revealed similar, although less statistically powerful results (see Supplementary Table 2).
