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Dear Dr. Muthen, I would like to ask if the current Mplus can fit the DiggleKenward selection model and/or sharedparameter model for nonignorable missing data. If yes, is there a code example that you may show me? Thank you. 


By DiggleKenward, do you mean missingness that is a function of the variable which has missingness? If so, yes. If not, please send me the article describing the model. 

V X posted on Wednesday, October 10, 2007  3:02 am



Dr. Muthen, I am also interested in learning Mplus to fit DiggleKenward selection model and sharedparameter model for nonignorable missing data (that is, missing not at random). Would you provide some Mplus code examples? Thank you. 


I think DiggleKenward consider missingness as a function of the (latent) response variables y  what you would have observed if if wasn't missing. You could use DATA MISSING to create binary missing data indicators and then regress those "u's" on the "y's" that have missingness by regular ON statements (y on u). I am not familiar with the term "sharedparameter model". 


P.S. For more nonignorable missing data modeling, see also the Lecture 17 handout from my UCLA latent variable course at http://www.gseis.ucla.edu/faculty/muthen/Handouts.htm 

V X posted on Friday, October 12, 2007  1:00 pm



Dr. Muthen, the Lecture 17 notes for Educ 231E class helps me a lot to understand the "outcome based dependent selection model". Thank you. Currently, I have one question about the path diagram of "growth mixture model with nonignorable missingness as a function of y". I know a circle represents a latent variable. Would you intepreate that, what the meaning of putting a circle on y in the diagram under the contents of selection model? 


For individuals who have missing data on y, the y variable is a latent variable. 


Dr Muthen, You said "for individuals who have missing data on y, the y variable is a latent variable". Do you mean that by creating a latent variable CY like the figure in slide 6 of your Lecture 17, we can fit a nonignorable missingness model with missing Y? 


Yes. But you can also do nonignorable missingness modeling without cy (which is categorical) using the model of slide 3. Neither is an easy thing to do. 


Dear Dr. Muthen, I was wondering if the syntax for the models that you discuss in the slides http://www.gseis.ucla.edu/faculty/muthen/Handouts.htm (Lecture 17) is available. Kind Regards, Liesbeth 


No, it is not. But a new missing data paper will be posted within short which discusses alternative models for nonignorable missing data and you can then request the Mplus setups for those analyses. 

Tim Stump posted on Friday, April 20, 2012  3:01 pm



I have a cohort of type II diabetes adolescents with hemoglobin a1c collected at baseline (prior to high school graduation), 3, 6, 9, and 12 month time pts. We know that our a1c outcome does not satisfy MAR assumption because we could not get all chart review data from physicians offices after adolescents left home. Baseline a1c is not missing, but missing increases over time. The cohort is relatively small with 180 subjects, but would like to explore some of the models outlined in "Growth Modeling With Nonignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial". Our goal is simply to model a1c over time and see if trajectory is different for a couple of binary covariates and if missing a1c influences trajectory. Would have you any suggestions as to which type of NMAR model would work better with our small sample? 


Patternmixture modeling is probably the easier to work with. 

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