Amy Hartl posted on Wednesday, September 26, 2012 - 9:09 am
I see. Okay, thank you!
Amy Hartl posted on Wednesday, September 26, 2012 - 11:37 am
I see that loading all of the indicators @1 enforces the proportional odds assumption. How can I test the constant hazard rate assumption, i.e., how can I constrain the hazard rate to be equal across time?
Can I do this without using type=mixture and a latent class design?
Hi, I try see how the change of family cohesion during middle school influences high school substance use. The former was modeled as a growth curve and the later was a discrete survival. How can I combine the two in one model? I want to see if the family cohesion change will influence survival function. Is my syntax right?
variable: names are age income sex sub4 sub5 sub6 w1f w2f w3f; usevariables are age income sex w1f w2f w3f sub4 sub5 sub6; categorical are sub4 sub5 sub6; missing is blank; classes=c(1); analysis: type=mixture; starts=100 10; ALGORITHM=INTEGRATION; model: %overall% if sf|w1f@0w2f@1w3f@2; sd4-sd6 on if sf(1); sd4-sd6 on age sex income(2);
Wen-Hsu Lin posted on Wednesday, May 20, 2015 - 7:25 pm
Thank you Dr./Prof. Muthen
May I ask one follow up. The effect of all the covariates on the survival function is modeled on the on statement right? The explanation of such coefficient is similar to those we would get in the regular survival analysis right (i.e., the increase one unit in a covariate will increase the risk of experiencing the event)? Thank you.