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Dear Dr. Muthen, I am a Ph.D. student working on an etiological model of criminal recidivism. Therefore, I want to do a path analysis where the main dependent variables are three types of criminal recidivism, each defined as a dichotomous variable (no/yes) with a specific followup time. In a regular regression analysis, I would use Cox regression. But because I want to do an etiological model, I must use path analysis (or SEM). My question is the following: is there a way to take timetoevent into account in a path analysis? Here's a simplified version of my syntax (without time): TITLE: ... DATA: FILE IS "..."; VARIABLE: NAMES ARE delsex delgen youthstr sexrec viorec nsnvrec; USEVARIABLES ARE delsex delgen youthstr sexrec viorec nsnvrec; CATEGORICAL IS sexrec viorec nsnvrec; MODEL: sexrec ON delsex delgen youthstr; viorec ON delgen youthstr; nsnvrec ON delgen youthstr; OUTPUT: standardized; If I wanted to take the time elapsed until recidivism into account, how would I proceed? The important thing to remember is that each recidivism type (sexrec, viorec, nsnvrec) has its own followup time. Thanks a lot for your time, your software is fantastic! 


P.S. I already have the "time" variables in my database, formatted for SPSS use (Cox regression). What I want to know is how to integrate them in the path analysis syntax. Thanks again! 


This modeling is possible. The User's Guide has several examples of Cox regression (ex6.20  ex6.22). I don't think the results are different when running each of your 3 DVs together compared with 1 at a time. 


Thanks for the fast answer! Then, my question becomes: how would I modify the 6.20 example to account for multiple timedependent DVs? In the manual, we have: VARIABLE: NAMES = t x tc; SURVIVAL = t (ALL); TIMECENSORED = tc (0 = NOT 1 = RIGHT); ANALYSIS: BASEHAZARD = OFF; MODEL: t ON x; For multiple DVs, would it look like this? VARIABLE: NAMES = t1 t2 t3 x tc1 tc2 tc3; SURVIVAL = t1 t2 t3 (ALL); TIMECENSORED = tc1 tc2 tc3 (0 = NOT 1 = RIGHT); ANALYSIS: BASEHAZARD = OFF; MODEL: t1 t2 t3 ON x; Thanks again. 


That looks right  try it. Perhaps you want to add a factor measured by the 3 outcomes to make them correlate beyond their common dependence on x. 


Absolutely! This is just an oversimplified version of my model to make sure I got the part about multiple timedependent DVs right. In my real model, there are 3 IV predicting criminal recidivism, each with their own developmental antecedents. Thanks again, I'll let you know how the syntax worked! 

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