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 Moh Yin Chang posted on Saturday, July 21, 2007 - 2:00 pm
Dear Dr. Muthen,

May I know if Mplus can handle joint modeling of survival and repeated measure (growth model) distribution? What is I also want to model discontinuity in the repeated measure process?

Thanks.

Moh Yin
 Tihomir Asparouhov posted on Monday, July 23, 2007 - 10:09 am
Mplus can handle joint modeling of survival and repeated measure. You can use either continuous or discrete time survival modeling. This modeling is essentially NMAR analysis, so it could be tricky but powerful.

Tihomir
 Anna Zajacova posted on Friday, December 04, 2009 - 3:41 pm
Hi,
I am also interested in a joint model of a repeated-measure outcome (5-category ordered variable) and survival in a data with 70% dying during the 4-wave 7-year study.
May I ask where I could find an example of implementing the joint survival/growth model in Mplus?
Many thanks in advance for your response -- this discussion forum is immensely helpful!!!
 Bengt O. Muthen posted on Sunday, December 06, 2009 - 10:58 am
One way to handle this is to follow the UG examples 6.23. Just replace the f, u part with your growth model where f would be the growth factors.

That's not the only way to do this, however. You can also study e.g. the Diggle-Kenward 1994 Applied Statistics "selection" modeling approach to NMAR, the Roy 2003 Biometrics pattern-mixture oriented approach, and the Beunckens et al 2008 Biometrics shared-parameter approach. The Beunckens approach is similar to ex 6.23 in the 1-class case. These approaches and many more can be handled in Mplus as I show in an upcoming paper. The question is how you view the relationship between death, your outcome, and other related variables.
 Anna Zajacova posted on Wednesday, December 09, 2009 - 11:25 am
Dear Dr. Muthen,
Many thanks for your prompt and helpful reply!
I will consult the sources you suggested.
Respectfully,
Anna Zajacova
 Nancy Rumbaugh Whitesell posted on Thursday, November 18, 2010 - 9:09 am
I have longitudinal data on the onset of substance use across four substances -- cigarettes, smokeless tobacco, alcohol, and marijuana. I have estimated discrete-time survival models for each substance separately and would like to model the relationship among hazards across substances, analogous to a parallel-process model of multiple LGCM trajectories. I am uncertain that I have done this correctly and would like to confirm before I interpret. Here is the code:
analysis: estimator = mlr; integration=montecarlo;
model: hazc by cig9-cig14@1;
hazt by tob9-tob14@1;
haza by alc9-alc14@1;
hazm by mar9-mar14@1;
hazc hazt haza hazm on sexf;
 Bengt O. Muthen posted on Thursday, November 18, 2010 - 5:33 pm
You want to check that your 5 haz factors are correlated conditional on the covariate - check your output and if not add WITH statements.
 Michael S. Businelle, Ph.D. posted on Thursday, January 12, 2012 - 8:31 am
I am conducting a discrete time survival analysis (example 6.19 in edition 5 of the MPlus manual).

I have 4 differet time points where relapse was determined (abstinent = 0, relapse = 1, missing = 999). It seems that example 6.19 instructs me to code all time points after the first relapse as missing. Is this correct?

Thanks,
Michael
 Linda K. Muthen posted on Thursday, January 12, 2012 - 9:46 am
Yes, this is correct. You will find more information about discrete-time survival analysis in the Topic 4 course handout on the website starting at slide 132. Following are examples of how the data should look for discrete-time survival analysis:

An individual who is censored after time period five ( ji = 6)
( 0 0 0 0 0 )
An individual who experiences the event in period four ( ji = 4)
( 0 0 0 1 999 )
An individual who drops out after period three, i.e. is censored
during period four before the study ends ( ji = 4)
( 0 0 0 999 999 )
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