A Comparison of One-Step and Three-Step Approaches for Including Covariates in the Shared Parameter Growth Mixture Model

Abstract

The shared parameter growth mixture model (SPGMM) has been proposed as a method to handle missing not at random (MNAR) data in longitudinal studies. This Monte Carlo simulation study compared the one-step approach with a three-step approach for adding covariates into the SPGMM. The results showed that performances of one-step and three-step approaches did not differ, but the estimate of the coefficient of the covariate was biased in most conditions with MNAR data. However, means, variances, and covariance of the intercept and slope as well as their standard errors were estimated without bias in most conditions, except for some combinations of small class distances and MNAR dropout missingness that was not related to the underlying growth trajectory. Classification accuracy was similar with both one-step and three-step SPGMM.

Keywords:

• missing data
• missing not at random
• shared parameter growth mixture models
• one-step and three-step approaches

Notes

¹ Tables with the η² of all main effects and interactions of manipulated conditions, as well as complete tables of relative bias, are available by contacting the first author.