A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome.

ABSTRACT: Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian

SUBMITTER: Kim SY 

PROVIDER: S-EPMC7497844 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature