Message/Author |
|
Lois Downey posted on Thursday, June 18, 2009 - 11:03 am
|
|
|
Before the release of Mplus 5.21, I ran a survival analysis that produced a loglikelihood of -292.574 and a parameter estimate of -.638 (p=.007) for my predictor of interest. With Mplus 5.21, the same model generates a loglikelihood of +6346.678 and a parameter estimate of -.102 (p=.173) for my predictor of interest. Most other models that I have compared from before and after the Mplus 5.21 release look identical. The substantial change in this model may reflect the fact that I have a relatively small sample (n=488) and a reasonably large number of free parameters (17). Do you find the difference in results believable, and would you advocate accepting the Mplus 5.21 result? |
|
Lois Downey posted on Thursday, June 18, 2009 - 2:45 pm
|
|
|
As an amendment to my earlier posting, I would note that at least part of the problem may be the result of the fact that I have observations with survival time of 0. Do the two versions of 5.21 handle survival times of 0 differently? |
|
|
Please send the two outputs, your data, and your license number to support@statmodel.com. |
|
Michael posted on Wednesday, March 07, 2012 - 12:52 pm
|
|
|
I am attempting to use a discrete-time survival model to examine initiation of substance use. I have a single, dichotomous outcome variable (substance use initiation), assessed at 5 time points. I would like to include both time-invariant (e.g., gender) and time-varying (e.g., stressful experiences) covariates, also assessed at the 5 time points. The central question I am interested in is whether recent stressful experiences represent a risk factor for substance use initiation. Following are the VARIABLE and MODEL statements I am working with. VARIABLES ARE SUW1-SUW5 !Substance use initiation, coded 0 (no), 1 (yes), and 999 (missing or yes at a previous wave) Gender !coded 0 = female, 1 = male STW1-STW4 !Past year stressful experiences (continuous) MODEL: f by SUW1@1 SUW2@2 SUW3@3 SUW4@4 SUW5@5; f@0; f on Gender; SUW2 on STW1 (A); SUW3 on STW2 (A); SUW4 on STW3 (A); SUW5 on STW4 (A); Have I included the time-varying covariates correctly? I constrained them to be equal across the waves, as it does not seem necessary to unconstrain them for what I am doing. Your feedback is greatly appreciated. |
|
|
It looks like you are using the time-varying covariate correctly but I wonder about your BY statement. It should be f by SUW1-SUW5@1; See Example 6.20 in the user's guide. |
|
Michael posted on Thursday, March 08, 2012 - 3:44 pm
|
|
|
Thank you for your response. The @1-5 was an error from reducing the statement to post here. I was thinking survival, but it seems that my fingers liked growth better. Thanks again. |
|
|
Hello, I may have a situation that is similar to Michael's. I have 5 time points of a discrete outcome, and 2 time varying covariate that corresponds to each of the 5 outcome time points. Here are my questions. 1) Is it possible to have interaction terms between the time varying covariates? e.g. OutcomeT2 on CovariateA_T1 CovariateB_T1 CovariateA_T1*CovariateB_T1; 2) There are theoretical reasons to believe that each time varying covariate should be correlated with itself at the next time point (CovariateA_T1 CovariateA_T2 etc). How would that be written into the survival analysis syntax? What I would really like to try to do is have a path analysis that ends in a survival function, with the caveat that the path model varies over time. Can that be accomplished in mplus? Or would such a scenario be better modeled in mplus but using a different framework like SEM or a multilevel model with a within subjects time factor for the first level? |
|
|
1) sure 2) All covariates are correlated - but this marginal part of the model is not estimated in the model (can be estimated by sample statistics). Use wide format (single-level) analysis to get max flexibility - for instance allowing path coefficients varying over time. |
|
Back to top |