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.