By Diggle-Kenward, 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 Diggle-Kenward selection model and shared-parameter model for nonignorable missing data (that is, missing not at random). Would you provide some Mplus code examples?
I think Diggle-Kenward 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 "shared-parameter model".
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?
No, it is not. But a new missing data paper will be posted within short which discusses alternative models for non-ignorable 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?