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| Non-ignorable, non-monotone missing data |
 
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| I have an advanced cancer quality of life dataset, where missing data is not only non-ignorable, but also non-monotone as at some time points subjects missed filling out a question but didn't drop out of the study. Is there any literature to describe how to model such missing data that is non-monotone in MPlus? Thank you! |
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Both monotone and non-monotone missing data can be handled in Mplus with ML under the standard MAR assumption. Non-ignorable missing data, that is non-MAR (NMAR), is more typically modeled to account for dropout, that is monotone missingness. The assumption probably is that non-monotone missingness is more likely to be MAR and NMAR methods are mostly needed for monotone situations.
I discussed NMAR modeling in
Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33.
These models also take care of non-monotone missingness that is MAR.
NMAR modeling for non-monotone missingness is also possible, say using pattern-mixture techniques (see paper). |
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Hello,
I'm trying to follow the Star*D analysis.
I started with the run2 (http://www.statmodel.com/examples/stard/run2.out) syntax.
But receive the error
*** ERROR in DATA MISSING command
The number of variables specified for NAMES must be equal to the number
of BINARY variables for TYPE=MISSING.
My code is
Missing = all (-999);
USEVAR ARE GH1 GH2 GH3 GH4 GH5
GH6 GH7 GH8 GH9 GH10
u2 u3 u4 u5 u6 u7
u8 u9 u10;
Data missing:
names = GH1 GH2 GH3 GH4 GH5 GH6 GH7 GH8 GH9 GH10;
binary = u2 u3 u4 u5 u6 u7 u8 u9 u10;
I can't find much help online for the data missing command. Any links or thoughts would be much appreciated. |
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| P. 530 of the UG version on our website describes this and agrees with the error message you got: you have 9 u's and 10 GHs but there should be 10 u's. |
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