Message/Author 


Dear Drs. Muthen, I am trying to run a joint model with a survival analysis to evaluate the effect of a factor on survival. I am evaluating the association of literacy (literate/illiterate) on risk of incident dementia, and seeing whether death or dropout affects this relationship. I was able to get the model to run using a similar example on chapter 6 of the user guide (ver 8), but the estimate I am getting for the death/dropout factor is so huge it makes me believe it is incorrect. The syntax is as follows: missing are all (999); usevariables are demtime demstat lit ndv2ndv5; useobservations are demtime ge 0.00000001; survival = demtime; timecensored = demstat (1 = NOT 0 = RIGHT); ANALYSIS: BASEHAZARD = on; MODEL: F by ndv2ndv5@1; F@0; demtime on lit F; F on lit; output: cinterval standardized; The estimates in the output are: F ON LIT 0.005 (SE = 0.003) DEMTIME ON F 40.969 (SE = 13.855) DEMTIME ON LIT 0.921 (SE = 0.274) The estimate of 40 for demtime on F seems incredibly large. Especially because when I exponentiate it to get the hazard's ratio is 620312637569017000.00. What am I doing wrong? I appreciate the help! Best, Miguel 


I think you should replace F by ndv2ndv5@1; F@0; with F by ndv2ndv5; otherwise F=0. 


Thanks for the suggestion, but when I do that I get the following error: " THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILLCONDITIONED FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NONPOSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.160D11. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 6, F BY NDV4" That's why I had chosen to constraint the model @1 in order to make it converge. 


In principle the model is identified but maybe there is something specific to your data that is causing the issue. Try removing only the line F@0; 


After removing F@0; I continue getting a similar estimate and SE. It can't be that that estimate is correct, right? I use a similar syntax to jointly model that death/dropout factor on latent growth curve model and the estimates are appropriate there. 


Forgot to mention, the LGCM uses the tscore function given that the participants have individually varying time points. Not sure if that may play a role in why that factor works one way in the LGCM and another here. Thanks! 

Back to top 