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.
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!!!
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.
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;
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?
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 )
Dear, I want to run a continuous-time survival analysis using a Cox regression model. In doing so, my outcome is continuous and longitudinal (sitting, measured 5 times- s1-s5). 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 s1-s5 x y tc; SURVIVAL = t (ALL); TIMECENSORED = tc (0 = NOT 1 = RIGHT); MODEL: i s | s1@0s2@1s3@2s4@3s5@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.
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...
ANALYSIS: TYPE = mixture; !PROCESSORS = 2;STARTS = 250 100; STITERATIONS = 100; ALGORITHM=INTEGRATION; MODEL: %OVERALL% i s q | s3sitm@0s4sitm@1s5sitm@2s6sitm@3s7sitm@4s8sitm@5; !s3sitm-s8sitm pon no_dis_0-no_dis_5 ; !s3sitm-s8sitm pon exergr_0-exergr_5 ; !s3sitm-s8sitm pon smok_0-smok_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;
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.
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
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?
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.