Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework

This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships between growth factors and covariates. To combat this issue, this study introduces the Generalized Tobit estimator (GBIT), an advancement of the conventional Tobit model, designed to handle mixed censoring effects in longitudinal data. The objectives of this study were threefold: (a) to develop GBIT for doubly-censored data, (b) to evaluate GBIT's performance in LGCMs under mixed censoring, and (c) to examine the impact of such censoring on covariate effects and outcomes within LGCMs. A Monte Carlo simulation was conducted to assess GBIT's effectiveness to handle doubly-censoring effects in the LGCM framework, demonstrating its ability to provide unbiased estimates even in the presence of significant censoring. Also, GBIT was applied for empirical data positing doubly-censoring effects, further supporting the use of GBIT, particularly in situations involving doubly-censored data.