Discrete Time Survival Analysis plots PreviousNext
Mplus Discussion > Categorical Data Modeling >
Message/Author
 Amy Hartl posted on Saturday, September 22, 2012 - 9:22 pm
Greetings,

I'm running a DTSA and would like to view the survival and hazard plots. I specify Plot2 in the output command, but it says the only plots available are:

Histograms (sample values, estimated factor scores)
Scatterplots (sample values, estimated factor scores)
Item characteristic curves
Information curves

Are survival plots only available for continuous time SA? If not, how can I obtain these?

I am following example 6.19 from the manual and am running version 6.11.

Thank you!

Amy
 Linda K. Muthen posted on Saturday, September 22, 2012 - 9:40 pm
You need to use the DSURVIVAL option of the VARIABLE command to specify which variables are the discrete-time survival variables. Then you will get the plots.
 Amy Hartl posted on Sunday, September 23, 2012 - 3:13 pm
Great, thank you! This yielded the estimated baseline survival curves. Is there a way I can get the estimated baseline hazard curves? It won't let me select it when I'm the plot menu.

Below is the syntax in case that's useful.

Thank you!


TITLE: DTSA time and fship freq only
DATA: FILE IS rec7to12.dat;

VARIABLE: NAMES ARE
dyadn
f1
f2
freqF1
freqF2
u7
u8
u9
u10
u11
u12
;

USEVARIABLES=
freqF1 freqF2 u8-12
;
CATEGORICAL = u8-u12;
dsurvival= u8-u12;
MISSING ARE ALL (9999.00);

ANALYSIS:
ESTIMATOR = MLR;

MODEL:
f BY u8-u12@1;
f ON freqF1 freqF2;
f@0;

OUTPUT: sampstat; stdyx;
PLOT: TYPE IS Plot3;
 Linda K. Muthen posted on Monday, September 24, 2012 - 12:09 am
That plot is for continuous-time survival.
 Amy Hartl posted on Wednesday, September 26, 2012 - 3:09 pm
I see. Okay, thank you!
 Amy Hartl posted on Wednesday, September 26, 2012 - 5:37 pm
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?

Thank you for your help!
 Bengt O. Muthen posted on Wednesday, September 26, 2012 - 11:39 pm
You can run the model saying e.g.

u1-u5 on x;

versus

u1-u5 on x (1);

The latter approach is the same as saying

f by u1-u5@1;
f on x;

Twice the loglikelihood difference for the 2 models gives a chi-square test.
 Wen-Hsu Lin posted on Tuesday, May 19, 2015 - 12:54 pm
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@0 w2f@1 w3f@2;
sd4-sd6 on if sf(1);
sd4-sd6 on age sex income(2);

Thank you.
 Bengt O. Muthen posted on Wednesday, May 20, 2015 - 12:34 am
You say

sd4-sd6 on if sf(1);

but those sd vbles are not in your usev list. They need to be scored as discrete-time survival variables. And you may want to specify

f by sd4-sd6@1;

f on age sex income;
 Wen-Hsu Lin posted on Thursday, May 21, 2015 - 1:25 am
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.
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