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 Anonymous posted on Sunday, June 27, 2004 - 1:07 pm
Is there anyway way to model multilevel data in discrete-time survival analysis? Our sample consists of siblings coming from the same families. Thanks.
 bmuthen posted on Sunday, June 27, 2004 - 4:15 pm
Yes you can do this - in two ways. One way is to do a single-level analysis where you model the variables for all of the siblings (the sample size is the number of families). The other way is to do a two-level analysis with siblings nested within families, using random effects (e.g. a random intercept) that vary across families
 Hanno Petras posted on Wednesday, July 28, 2004 - 12:38 pm
Hi Bengt,

as a follow-up to Anonymous's question, I assume that you would have to use the "f by event indicator notation" instead of the LCA parameterization. Would "f" then be allowed to vary across clustering units? Also, is there an example output file available? Thanks.

Best,

Hanno
 bmuthen posted on Wednesday, July 28, 2004 - 1:37 pm
Yes. Ex6.19 in the Version 3 User's Guide would have to be combined with the examples of the multilevel chapter 9, say ex9.6, but perhaps with only the fb factor on between.
 Chyke Doubeni posted on Monday, September 14, 2009 - 10:41 am
This is a somewhat naive question but will appreciate feedback and guidance.

We are estimating two-level survival analyses and need to get estimates of the random effects - variance, interquartile hazard ratio (HR) and median HR. This is discussed in the article: Chaix & Merlo. Am J Epidemiol 2005;162:171182.

My collaborators used a Bayesian approach in SAS to estimate the variance parameter. How do I do specify it in MPLUS to get estimates of the random effect?


This is the current form of the model. I had also specified it as a discrete time model.

WITHIN = ... ;

BETWEEN = x2-x5;

CLUSTER = tractid;
SURVIVAL = p_yralld (ALL);
TIMECENSORED = deadcnsr (1=NOT 0=RIGHT);

ANALYSIS: TYPE = TWOLEVEL;
BASEHAZARD = OFF;
ALGORITHM = INTEGRATION;
INTEGRATION = MONTECARLO;
ESTIMATOR=ML;
PROCESSORS=2(STARTS);

MODEL:

%WITHIN%
p_yralld ON entr_age sex bmicur marriage ltcoll coll raceblk raceoth diabetes heart stroke smoke logcal logfiber fairpoor;

%BETWEEN%
p_yralld ON x2-x5;
 Bengt O. Muthen posted on Tuesday, September 15, 2009 - 9:15 am
In your setup you estimate on Between the residual variance of the random intercept for the p_yralld survival variable. If you delete x2-x5 as covariates on Between, you will estimate the variance of the random intercept.
 Chyke Doubeni posted on Friday, September 18, 2009 - 2:08 am
Thank you so much. I modified the model as follows and used MLR:

BETWEEN = x2-x5;
......
ANALYSIS: ....
ESTIMATOR=MLR;

MODEL:

%WITHIN%
p_yralld ON entr_age ;

%BETWEEN%
p_yralld ON ;

1.) The standardized variances were 1.0 and se=0. Should I expect that?
2.) Also, I modeled quintiles of the BETWEEN variable. What would be your advice for deriving the IHR, and Median HR?
3.) The formula in Chaix & Merlo's article seems to be based on continuous rather than dummies.
Would I need to use the continuous BETWEEN variable rather than the "quintile" variables?
 Linda K. Muthen posted on Friday, September 18, 2009 - 3:24 pm
Regarding number 1, please send the output and your license number to support@statmodel.com.

Regarding 2, 3, and 4 I am not familiar with the paper.
 Chyke Doubeni posted on Tuesday, September 22, 2009 - 9:54 am
I think I figured it out. I was looking to derive the random effects (frailty) parameter with Cox frailty models. This is not possible in the multilevel framework - is it?
 Bengt O. Muthen posted on Tuesday, September 22, 2009 - 10:53 am
Yes, you can have frailties in the Cox model, and on both levels.
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