Best way to handle missing data?

I don't think the model I am working on is a good candidate for structural
equation modeling because the data set is very unbalanced (ie. there are
very different numbers of observations for different people, taken at
different times), the main relationship of interest involves a time-varying
predictor, and one of the variables with missing data is not continuous (it
is a binary, categorical variable).  So, I will stick with the multiple
imputation approach for handling the missing data.

As Wolfgang mentioned, OpenMX can fit a FIML analysis to irregular data. 
If you were, for example, interested in a profile likelihood around a 
variance component, that might be the way to go.  It seems to me that 
multiple imputation might not always respect complicated 
clustering/correlation, depending on the actual method. A quick search 
found some cautionary tales in:

http://www.bmj.com/content/338/bmj.b2393.extract

Just another 2c, David.


| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: -0101
| Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A