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.
Hello! I just finished running a survival mixture analysis. I wanted to get a sense of the output.
NO COVARIATES: 1. I am assuming that the value under "means" for my survival variable are the log odds? so values below 1 (e.g., 0.414) represent a lower hazard of experiencing the event? 2. Is there a way to test differences between survival curves across classes?
COVARIATE: 3. when I add a covariate predicting class as well as survival - what exactly is the interpretation of the regressions for T on X; C on X;
and should I simply investigate the odds ratio's (under 'categorical latent variables') for C on X?
Thanks Dr. Muthen, So, after reading the paper you and Dr. Maysn wrote on DTSA I've switched over. Instead of doing a GMM-DTSA I'm just doing an LCA-DTSA (3-class solution). my discrete survival time is by AGE (14-25 years) so I have data set up in an accelerated longitudinal framework.
my questions are: 1) I'm using LOGRANK to test differences in survival curves - is this appropriate? 2) does Mplus provide median surivial time? by class? 3) if I add F on PTSD12 in the OVERALL statement is this a a hazard ratio? even though the effect is the same across classes? 4) When plotting if I choose to plot "esitmated survival curves" and choose PTSD at value 1 does this adjust my curves for the co variate?
Thank you for this info - very useful! I also found Dr. Muthen and Maysn's article on DTSA to help with the interpretation of a co variate on survival time. I suppose I could use exp(B) and get the hazard ratio as well?
in regards to the basehazard output. my survival time is by "age" from 14 - 25 years old. the output gives me baseline survival rate. Is it ok to simply count from 14 at T1 and say, "at 18 years old, class 1 had a survival rate of XX%" Baseline Survival Rates for Class 1 are below: thus at 18 years old survival rate was 19%? TIME SURVIVAL RATE
There are two extensions of the continuous PH model to discrete times 7.5.2 (logit what Mplus does) and 7.5.3 (complimentary log-log not available in Mplus), and hazard is defined differently than in the continuous case. It is the logit of the hazard that is proportional in 7.5.2. Depending on the point of view either of the two can be preferred to be the natural extension of Cox PH model.
Your reading of the basehazrd output is correct. The median is age 17 (pass age 16 only 0.506 survived). Every number in the second column is doubled as to show the interval if you are to plot the KAPLAN - MEIER curve - it makes it a little hard to read.
I am interested in generating standard errors for the estimated survival probabilities in a DTSA with 8 time intervals (at select covariate values).
I attempted to do so in Mplus (ex. 6.19) using MODEL CONSTRAINT. First, I generated the conditional hazard probabilities at select covariate values using the formula provided by Masyn (2014): 1 / (1 + exp(-(-threshold for Dx + b1*x + b2*m + ...)), where Dx = the unique thresholds for the D1-D8 survival variables.
Second, I converted these into survival probabilities by multiplying the complements of the hazard probabilities for each time period. E.g., S3 = (1 – Ph1)*(1-Ph2)*(1-Ph3), where Ph = the conditional hazard probability for each interval.
The issue is that the conditional hazard probabilities do not match those obtained using other methods (e.g., Singer & Willett, 2003). The hazards generated in Mplus appear to consistently be slightly too small. Is there an error in my formula for the hazards? The consistent underestimates of the hazards implies there may be an error with the threshold portion of my formula, but I am not sure.
Any clarification you can provide would be much appreciated. Alternatively, if there is a better method to generate the standard errors of the predicted probabilities in Mplus, please let me know.
Here is how you can do this in User's Guide example 6.19, for X=1. The parameters k1-k4 give you the estimated kaplan-meier curve.
TITLE: this is an example of a discrete-time survival analysis DATA: FILE IS ex6.19.dat; VARIABLE: NAMES ARE u1-u4 x; CATEGORICAL = u1-u4; MISSING = ALL (999); ANALYSIS: ESTIMATOR = MLR; MODEL: f BY u1-u4@1; f ON x (b); f@0; [u1$1-u4$1] (t1-t4); output:tech10 residual; model constraint: new(p1-p4); p1=Exp(t1-b)/(1+Exp(t1-b)); p2=Exp(t2-b)/(1+Exp(t2-b)); p3=Exp(t3-b)/(1+Exp(t3-b)); p4=Exp(t4-b)/(1+Exp(t4-b)); new(s1-s5); s1=1-p1; s2=p1*(1-p2); s3=p1*p2*(1-p3); s4=p1*p2*p3*(1-p4); s5=p1*p2*p3*p4; new(k1-k4); k1=p1; k2=p1*p2; k3=p1*p2*p3; k4=p1*p2*p3*p4;