Anonymous posted on Sunday, June 27, 2004 - 7:07 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 - 10:15 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
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.
bmuthen posted on Wednesday, July 28, 2004 - 7:37 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.
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;162:171–182.
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.
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.
Thank you so much. I modified the model as follows and used MLR:
BETWEEN = x2-x5; ...... ANALYSIS: .... ESTIMATOR=MLR;
%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?
I am working on a multilevel survival analysis using cox regression (continuous time survival). The researcher I am working with have found meaningful person level predictors of returning to hospitalization (only the first return to treatment). The researcher would like to test to see if there is a random effect of hospitals, in particular to determine if there are different survival rates between hospitals.
I have received some recommendations that it may be best to dummy code each hospital (N=30) and include them in the analyses as a person level predictor, but I have not seen any literature to necessarily support that approach. I am wondering if there is a way to test if there are differences between hospitals (between level variables) on survival. An added difficulty is that there are no other hospital level predictors in the model.
Here is the syntax for the model I have proposed to test, but I realize that the variance estimation at the between level does not answer my question if survival differs as a function of hospitals:
ANALYSIS: TYPE = TWOLEVEL; BASEHAZARD = off; ALGORITHM=Integration; MODEL: %WITHIN% Days_Ret ON age race11 race3 R_arr3 ExtBeh Intern CareIss1 los_627; %BETWEEN% Days_Ret;