Message/Author 

Anonymous posted on Sunday, June 27, 2004  1:07 pm



Is there anyway way to model multilevel data in discretetime 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 singlelevel 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 twolevel analysis with siblings nested within families, using random effects (e.g. a random intercept) that vary across families 


Hi Bengt, as a followup 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. 


This is a somewhat naive question but will appreciate feedback and guidance. We are estimating twolevel 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. WITHIN = ... ; BETWEEN = x2x5; 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 x2x5; 


In your setup you estimate on Between the residual variance of the random intercept for the p_yralld survival variable. If you delete x2x5 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 = x2x5; ...... 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? 


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. 


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? 


Yes, you can have frailties in the Cox model, and on both levels. 


Hello, 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; Thank you. 


Either fixed effect model with dummy variables or random effect model that you have above should illuminate the issue. 


Hi Bengt and Tihomir, as a followup to this conversation I have another question. I've never used Mplus and it can be my sofware for the future. I'm working on a multilevel frailty model in order to study survival of children. The structure implies a household (second) and regional (third) level. The measurement model is a 2level model with a latent variables at the household and regional level. These is then included as predictors in the 3level structural model for mortality I wonder if there is the possibility to include the variance of the householdlevel latent variable as a predictor in the structural model. In the case it could be done, is it feasible to include it in both the two and threelevel models? Any advice would be extremely useful! Thank you! 


You will need to use type=twolevel and the household variables have to be setup as a multivariate vector (wide format). Currently only twolevel modeling is available in Mplus with survival variables. To use the variance variable on the higher level, see section 4.1 in https://www.statmodel.com/download/NCME12.pdf or see slides 24 and 28 (two different approaches) in https://www.statmodel.com/download/handouts/MuthenV7Part2.pdf 


Is the twolevel continuoustime survival analysis using Cox regression with a random intercept model shown in Example 9.18 appropriate for use when levels correspond to repeated measurements of the same individual? Also, is this a frailty model? Thank you! 


Yes on both questions. 


Regarding example 9.18... If the clustering indicator is the individual, and it's a timetoevent outcome, how is this model estimating an individual random effect without another variable to estimate at the same time? (For example, event and death?) 


Take a look at sections 3.1 and 3.2 in http://statmodel.com/download/SurvivalJSM3.pdf The individual random effect (frailty) reflects the differences in the hazard functions and can be estimated due to the repeated measurements. The model in section 3.2 is a bivariate double frailty (one from id and one from hospital). To get the model for 9.18 you would use equation (21) only and remove eta_ijw. 


I think I am confused because there are two timetoevent variables in those examples (T1 remission to relapse and T2 relapse to death), but there is only one timetoevent variable in Example 9.18 (t). https://www.statmodel.com/usersguide/chap9/ex9.18.html 


What I don't understand is how you can estimate a frailty term if you never see T1 and T2 on the same individual, but you always see either T1 or T2. In other words, does a frailty model make sense when they are competing events? 


Frailty requires at least two observed time to event variables. This applies to twolevel methods like example 9.18 or single level multivariate methods like section 3.1. 


Thank you very much for that clarification. If I use example 9.18 with only one timetoevent variable (t), it is not a frailty model. Does this also mean that there is no random intercept? I want to use the exact specification below, clustering repeated measures within individuals, in order to accommodate timevarying confounders. VARIABLE: NAMES = t x w tc clus; CLUSTER = clus; WITHIN = x; BETWEEN = w; SURVIVAL = t (ALL); TIMECENSORED = tc (0 = NOT 1 = RIGHT); ANALYSIS: TYPE = TWOLEVEL; BASEHAZARD = OFF; MODEL: %WITHIN% t ON x; %BETWEEN% t ON w; t; 


Example 9.18 is a frailty model with random intercept because there are more than one time to event variables within each cluster, assuming the cluster size being more than one. I would recommend reading about "DATA WIDETOLONG:" command in Mplus to understand the equivalence between multivariate and multilevel models. This of course is not related to frailty  it is a general concept. 

Kirill Fayn posted on Wednesday, October 12, 2016  8:03 am



Hello, i am getting a new error on survival models that previously ran without any issues. The error is: *** ERROR There is at least one observation in the data set where a survival variable has a negative value or 0. Please check your data and format statement. There are indeed 0s in the variable but this has always been the case. There are negative numbers that have been defined as missing. The only thing that has changed is that I am running the analysis on 7.4 instead of 7. Thanks in advance for your help 


We made that change recently to improve the applicability of the model. Consider equation (8) in http://statmodel.com/download/Survival.pdf S(0)=1 meaning that the probability that a person dies at time T=0 is zero. Here are some options  I am not sure which one is best for you. a) Add +1 to the survival variable b) replace 0 with 0.0001 (this leads to huge hazard in the 0 to 0.0001 interval) c) remove these observations  it is not clear why you have these in the first place  we study a sample of alive people to see how covariats affect their survival, why include dead people in the sample. 

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