Message/Author 

Anonymous posted on Tuesday, April 12, 2005  6:33 am



Hello, I am trying to estimate a discretetime 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? Thanks! Tobias Here is the code so far: DATA: file is u:\initiationdata.dat; VARIABLE: NAMES ARE id gender u5 u6 u7 u8 u9 u10 u11 u12 u13 u14 u15 u16 u17 u18 u19 u20 u21; usevariables = gender u5 u6 u7 u8 u9 u10 u11 u12 u13 u14 u15 u16 u17 u18 u19 u20 u21; categorical = u5 u6 u7 u8 u9 u10 u11 u12 u13 u14 u15 u16 u17 u18 u19 u20 u21; MISSING = ALL(999); IDVAR is id; CLASSES = c(1); ANALYSIS: Type = Mixture Missing; !Starts = 100 10; Model: %overall% u5u21 on gender; %c#1% [u5$1*0 u6$1*0 u7$1*0 u8$1*0 u9$1*0 u10$1*0 u11$1*0 u12$1*0 u13$1*0 u14$1*0 u15$1*0 u16$1*0 u17$1*0 u18$1*0 u19$1*0 u20$1*0 u21$1*0]; OUTPUT: sampstat Tech1 Tech8; 

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? Thanks! 

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; Timevarying 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 support@statmodel.com. 


I am trying to reproduce the discrete time survival analysis examples in Singer & Willet (2003), chapter 12 [0], 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 [1] for input. 2. S&W describe the use of the complementary loglog 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? Thanks, Maarten [0] http://www.ats.ucla.edu/stat/examples/alda/ [1] http://pastebin.com/RV8hPURt 


1. Try fixing the variances of eta0 and eta1: eta0eta1@0; 2. Yes. 3. You can fix a threshold @0. 


Thanks. (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 DOSoutput I find the error: "forrt1: severe (164): Program Exception  integer divide by zero". The data an input are online[0], 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 TRAININGstatement does work (KNOWNCLASS does not), but I'm not sure why. Are there more examples/explanation available somewhere? [0] http://kanut.studentenweb.org/DTSA/ 


Please send the input and data for number 1 along with your license number to support@statmodel.com. 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 timevarying 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? I tried: f by u1u15@1; g BY u1u4@1; h BY u5u15@1; f on w; !cī path f on x; !b path g on m; h on m; !a path m on x; 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. Thank you very much! Luciana 

benedetta posted on Monday, February 09, 2015  7:44 am



Dear professors, I am conducting a discrete time survival analysis and I want to estimate confidence intervals for the hazard probability. In the simplest scenario where the only input are event indicators and latent class variable, mplus gives the thresholds and relative s.e.; when covariates (timeinvariant) are introduced, also logit coefficients and relative s.e. are estimated. Is it possible to use these estimates to compute the confidence interval for the correspondent hazard probability in the two cases? Thank you very much in advance! 


You can use Model Constraint for that. Just label the Model parameters involved in the hazard and use them in Model Constraint to express the hazard. That gives estimates and SEs. 

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