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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 


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 


Hi, I am also interested in a joint model of a repeatedmeasure outcome (5category ordered variable) and survival in a data with 70% dying during the 4wave 7year 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!!! 


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 DiggleKenward 1994 Applied Statistics "selection" modeling approach to NMAR, the Roy 2003 Biometrics patternmixture oriented approach, and the Beunckens et al 2008 Biometrics sharedparameter approach. The Beunckens approach is similar to ex 6.23 in the 1class 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. 


Dear Dr. Muthen, Many thanks for your prompt and helpful reply! I will consult the sources you suggested. Respectfully, Anna Zajacova 


I have longitudinal data on the onset of substance use across four substances  cigarettes, smokeless tobacco, alcohol, and marijuana. I have estimated discretetime survival models for each substance separately and would like to model the relationship among hazards across substances, analogous to a parallelprocess 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 cig9cig14@1; hazt by tob9tob14@1; haza by alc9alc14@1; hazm by mar9mar14@1; hazc hazt haza hazm on sexf; 


You want to check that your 5 haz factors are correlated conditional on the covariate  check your output and if not add WITH statements. 


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 


Yes, this is correct. You will find more information about discretetime 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 discretetime 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 ) 


Dear, I want to run a continuoustime survival analysis using a Cox regression model. In doing so, my outcome is continuous and longitudinal (sitting, measured 5 times s1s5). So i first run a growth curve model and then try to link that model with the mortality risk. I have two covariates x, and y. Am i correct with the following model? if not could please assist? VARIABLE: NAMES = t s1s5 x y tc; SURVIVAL = t (ALL); TIMECENSORED = tc (0 = NOT 1 = RIGHT); MODEL: i s  s1@0 s2@1 s3@2 s4@3 s5@4; i s t ON x y; the idea is then to predict t from i and s, after controling for x and y. 


This looks reasonable. You would need "t on i s" as well. In addition, you should use SURVIVAL = t; instead of SURVIVAL = t (all); The difference is explained in Section 9 http://www.statmodel.com/download/Survival.pdf That change will allow Mplus to use the most appropriate treatment for the survival variable. 


thank you, Tihomir. Its seem to work fine. I shall try adding a mixture part as well. I will post my proposal to that and imay have some follow up questions. Thanks! Borja 

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