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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 

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