Parallel process for non-time-strucut... PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Anonymous posted on Friday, February 27, 2004 - 1:59 pm
I am interested in doing a parallel process growth model like the one in example 22.3 on p.215 of the user's guide. However, I have non-time-structured data. I am wondering if TYPE RANDOM like example 5 on p.15 of the addendum can handle parallel process. In particular, does TYPE RANDOM allow regressions among the random effects in the two processes?

 Linda K. Muthen posted on Friday, February 27, 2004 - 3:14 pm
A model can have parallel processes with individually-varying times of observation and the random effects in the two processes can be correlated.
 Allison Tracy posted on Sunday, July 04, 2004 - 11:45 am
How is the intercept term interpreted when a growth model is estimated with individually-varying times of observation? And, as a related point, how does one calculate predicted values for the growth trajectories when the times of observation vary across individuals?
 bmuthen posted on Monday, July 05, 2004 - 9:31 am
The intercept is still interpreted as the systematic part of the growth model at the time when the time variable = 0. A typical example would be a treatment study where time is the number of weeks after treatment. So there is a single time variable measured in weeks, where different individuals have different values on this variable for each of the follow-up occasions after treatment. For example, the first follow-up (the second occasion) might be in week 1 for some, week 2 for most, and week 3 for some. Here, 0 would define the intercept at the treatment point. But you could also have the intercept be defined say at 8 weeks, in which case you would subtract 8 from each individual's time variable. Note that some individuals would then not have the time value 0 since not all individuals are observed at exactly week 8, but that's ok, the intercept is still defined at week 8.

As for average predicted values of the outcome, you still use the time variable (weeks in the example) - it doesn't matter that specific individuals are observed at different times. To check observed vs fitted curves for individuals, however, it makes sense to use the times the individual was actually observed.
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