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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 


Dear Tihomir, I have now run a growth mixture model and arrived to a solution that I am happy with. I would like now to use the c (profile) to predict mortality. would the following set up be ok? it does not work... SURVIVAL = fup_all_0; TIMECENSORED = status2_0 (0 = NOT 1 = RIGHT); CLASSES = c1(3); ANALYSIS: TYPE = mixture; !PROCESSORS = 2;STARTS = 250 100; STITERATIONS = 100; ALGORITHM=INTEGRATION; MODEL: %OVERALL% i s q  s3sitm@0 s4sitm@1 s5sitm@2 s6sitm@3 s7sitm@4 s8sitm@5; !s3sitms8sitm pon no_dis_0no_dis_5 ; !s3sitms8sitm pon exergr_0exergr_5 ; !s3sitms8sitm pon smok_0smok_5 ; i@0 s@0 q@0 i with s@0; i with q@0; s with q@0; fup_all_0 ON c1; OUTPUT: TECH11 TECH14; SAVEDATA: FILE IS growth_var=fix_cov=fix_2.dat; SAVE = cprobabilities; 


Sorry I forgot to mention that I have 25 dataset and using imputation to combine the results. Thanks! 


Take a look at User's Guide example 7.30. You have to replace fup_all_0 ON c1; with %C1#1% [fup_all_0@0]; %C1#2% [fup_all_0]; %C1#3% [fup_all_0]; 


Thanks, Tihomir: I have tried that syntax and it is basically reducing the number of classes to 1  I suppose those who survive. What I really want is to : A. Get profiles of people (based on sitting time) B. Predict mortality risk from class membership. Would that makes more sense? Thanks! 


I should have said that sitting time is a longitudinal outcome 


I think what you are saying is that the class formation changes when you add the survival variable. I would recommend the BCH method. See Section 3 http://statmodel.com/examples/webnotes/webnote21.pdf 


Ignore the above message  that is not available. I would recommend the 3step estimation, see Section 3 http://statmodel.com/examples/webnotes/webnote16.pdf 


Thanks. Would that work tho with Imputation type data? 


Ok, so it seems that the 3step estimation works, thanks! A follow up question...how do I use class 2 (for example) instead of class 1 as reference? I have tried  but does not work: %C1#1% [fup_all_0]; %C1#2% [fup_all_0@0]; %C1#3% [fup_all_0]; Thanks! Borja 


We need to see your full output  send to Support along with your license number. 


Dear professors, I have abandoned the latent class analysis and went back to joint repeated measures and survival outcome. I am having troubles in interpreting the results. the intercept is significantly associated with mortality and so is the slope. the quadratic is not. here are the resutls, how shall i clinically interpret them? long out is sitting time. FUP_ALL_0 ON I 0.151 0.030 5.022 0.000 S 0.641 0.299 2.142 0.032 Q 2.758 1.898 1.453 0.146 Means I 5.471 0.025 222.389 0.000 S 0.298 0.017 17.165 0.000 Q 0.055 0.003 16.696 0.000 


The interpretation is like in standard Cox regression. You can start with equation (8) http://www.statmodel.com/download/Survival.pdf It might be helpful to you in the interpretation if you save the factor scores for I, S and Q "savedata: file=1.dat; save=FS;" I would also recommend looking at "basehazard:output" as well as the plots available for your analysis using "plot:type is plot3;" 


thanks, Tihomir, It is still not clear to me what Q would mean in the context of survival. I can see what slope means  it could be effect of trend (faster or slower) on mortality. but not sure about Q interpretation. perhaps q can be used to figure out the longitudinal outcome at the turning point and use that value as a control alongside the final value of the outcome? or you are suggesting that we use factor scores to regress mortality on i s q and we interpret i and s? longitudinal outcome is sitting time thanks! B 


Since FUP_ALL_0 ON S is not significant you should delete it from the model. If it was significant it would mean that the relationship between survival and sitting time is more complex than just correlation (quadratic form). I did not suggest for you to use factor scores to regress mortality on. These are just estimates and they have measurement error and if you do that kind of regression you should expect biased estimates. I suggested that as a way to approximately understand / present and interpret your results. 


Correction: FUP_ALL_0 ON Q 


Thanks so much. I think it make sense. Borja 

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