Dynamic Structural Equation Modeling (DSEM) is a great tool to analyze intensive longitudinal data. Currently, I am working on a dataset of 64 participants and 30-50 timepoints. However, the data was collected over the course of 81-150 days. In other words, the time intervals between every two measurements are very uneven, ranging from 1 to 20. I know an AR(1) DSEM model in Mplus using MCMC imputation and Bayesian estimator and can produce a converged model. However, with the amount of missing data (70%-80%), are the results trustworthy? Thanks!
The best way to answer this question is probably by conducting a Monte Carlo simulation with the parameters/missing patterns that apply specifically to your application. You can save the parameter estimates from your application (that you're not sure you can trust) using the OUTPUT: SVALUES command in Mplus. This makes it very easy to set up a realistic population model for the simulation in Mplus using the MONTECARLO and MODEL POPULATION statements. You can also simulate missing data patterns in Mplus in the same simulation.
You may want to consider long format Random Effects modelling rather than wide format SEM modelling. Under the fairly forgiving MAR assumption it can handle imbalanced data; you can include continuous time in the fixed part of the model; and you can handle large numbers of repeats. See
Hamaker, E. L., & Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modelling. Psychological methods, 25(3), 365–379. https://doi.org/10.1037/met0000239
Article The fixed versus random effects debate and how it relates to...
Article Fixed and Random effects models: making an informed choice
and there are autoregressive versions
Chapter Autoregressive Linear Mixed Effects Models
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