Anonymous posted on Tuesday, April 12, 2005 - 6:33 am
I am trying to estimate a discrete-time survival model. I have 21 time points out of which respondents can change status in the last 17 (the first 5 are locked). I have estimated the model without covariates and I now want to add covariates to the model. How do I add time varying covariates? Also did I set the starting values corectly?
Tobias posted on Wednesday, April 13, 2005 - 5:04 pm
I changed the starting values to the conditional probabilities of the event to occur in each period. The model runs fine, but I do have problems with the starting values when I am adding demographic variables such as gender and race. How can I determine the appropriate starting values?
BMuthen posted on Wednesday, April 13, 2005 - 11:42 pm
If you are using Version 3.12, you should not need starting values at all. I would use STARTS = 50 5;
Time-varying covariates are added by saying ut ON xt; for each value of t.
If you continue to have problems, send you input/output and data along with your license number to email@example.com.
I am trying to reproduce the discrete time survival analysis examples in Singer & Willet (2003), chapter 12 , using the approach described in Muthen & Masyn (2005).
1. I can reproduce the zero order polynomial (constant) and completely general model, but I am not sure how to specify the linear, quadratic, etc. models in Mplus. I am using the syntax of Model 5 ('linear logit baseline hazard model') in Masyn (2003), but I do not obtain meaningful values (the means of the latent variables are 0). See  for input.
2. S&W describe the use of the complementary log-log link. In a old reply I read that cloglog is not supported in Mplus, is this still the case?
3. In R one can drop the intercept using a -1 in a formula, eg. glm(event~u7 + u8 + u9 + u10 + u11 + u12 - 1, family=binomial(link = "logit")). Is there an equivalent shorthand in Mplus?
(1) Regarding the baseline hazard form: if I fix the variances of eta0 and eta1 I get the message that I need to add ALGORITHM=INTEGRATION. If I add that however, Mplus will not provide output. In the DOS-output I find the error: "forrt1: severe (164): Program Exception - integer divide by zero". The data an input are online, I can email the licence number on Monday if needed.
(2) I am confused regarding the use of TRAINING vs. KNOWNCLASS when doing multiple group comparisons in a discrete time survival analysis. The TRAINING-statement does work (KNOWNCLASS does not), but I'm not sure why. Are there more examples/explanation available somewhere?
Please send the input and data for number 1 along with your license number to firstname.lastname@example.org. Send also the two outputs showing that TRAINING and KNOWNCLASS results differ. They should be the same. Please use Version 6 for all analyses.
Dear professors, I am running a path model with discrete time surv analysis, mediators and shared frailty. The prop hazards assumption is violated for M on Y but not for the X and control (W) variables.
I have 15 time intervals and it makes no theoretical sense for the effect of M to fluctuate in each interval. Under a continuous surv analysis framework I would estimate a piecewise constant hazard model, with M as a time-varying effect and W and X as time constant, imposing the cutoffs of the piecewise intervals to be the points where the hazards cross. Could something similar be done for a discrete model with Mplus?
1) Could this work or is this implementation incorrect? 2) A problem is that I get residual variances for f g and h and also correlations between f, g, and h. Can they be excluded? Do the correlations between f, g, h influence the coefficients or standard errors of the other estimates of the path model?
If it is impossible to estimate this I could allow all effects to fluctuate in g and h, although less ideal. And I also face the problem of the correlations between g and h.