A Comparison of LTA Models with and Without Residual Correlation in Estimating Transition Probabilities.

ABSTRACT: In longitudinal mixture models like latent transition analysis (LTA), identical items are often repeatedly measured across multiple time points to define latent classes and individuals' similar response patterns across multiple time points, which attributes to residual correlations. Therefore, this study hypothesized that an LTA model assuming residual correlations among indicator variables measured repeatedly across multiple time points would provide more accurate estimates of transition probabilities than a traditional LTA model. To test this hypothesis, a Monte Carlo simulation was conducted to generate data both with and without specified residual correlations among the repeatedly measured indicator variables, and the two LTA models-one that accounted for residual correlations and one

SUBMITTER: Lee NY 

PROVIDER: S-EPMC12356818 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature