05-04-2018
simonli
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03-14-2018
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Latest posts by simonli
Subject Views Posted 1536 05-03-2018 01:18 PM 1692 03-16-2018 11:34 AM 1720 03-14-2018 10:18 PM 1777 03-14-2018 11:36 AM -
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- Posted Proc MI warning message: The imputed variable in the MONOTONE statement is the leading variable xxxx on Statistical Procedures. 05-03-2018 01:18 PM
- Posted Re: Proc Mixed model statement - the order of covariates affect the standard error estimates? on Statistical Procedures. 03-16-2018 11:34 AM
- Posted Re: Proc Mixed model statement - the order of covariates affect the standard error estimates? on Statistical Procedures. 03-14-2018 10:18 PM
- Posted Proc Mixed model statement - the order of covariates affect the standard error estimates? on Statistical Procedures. 03-14-2018 11:36 AM
05-03-2018
01:18 PM
Dear all, I am trying to do multiple imputation, by treatment group. i am running the following code: proc MI data= raw seed=46485 nimpute=5 out= temp ; class trt01p ; var trt01p base month1 month3 ; monotone reg (month3 = base month1/details) discrim( trt01p /classeffects = include ) ; run; the log always came up with a warning message: WARNING: The imputed variable TRT01P in the MONOTONE statement is the leading variable in the VAR list. Missing values for this variable will not be imputed. What is the issue here? should I use 'by treatment' other than 'class treatment', and also do not include treatment in the var list and monmontone statement? Thanks very much SL
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03-16-2018
11:34 AM
Just want to add one more piece of information: I have tried the same model to generate multiple tables for different endpoints - it is odd that only one single table that had such problem: the order of the 'Base' in the model statement changed the standard error. All other tables had no difference.
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03-14-2018
10:18 PM
Yes; I tried to use DDFM=KENWARDROGER2 and the problem is the same. Thanks
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03-14-2018
11:36 AM
Dear all, Recently I was running sas proc mixed to validate my counterpart's results. Below is the statement I used (-note, 'Base' in the model statement): proc mixed data=indata method=reml; class TRTPN(ref='1') SITEGR1 AVISITN SUBJID; model CHG = BASE TRTPN SITEGR1 AVISITN TRTPN*AVISITN / DDFM=KENWARDROGER; repeated AVISITN / type=un sub=SUBJID; lsmeans trtpn*avisitn/pdiff cl ; run; My counterpart runs like this: proc mixed data=indata method=reml; class TRTPN(ref='1') SITEGR1 AVISITN SUBJID; model CHG = TRTPN SITEGR1 AVISITN TRTPN*AVISITN BASE / DDFM=KENWARDROGER; repeated AVISITN / type=un sub=SUBJID; lsmeans trtpn*avisitn/pdiff cl ; run; Surprisingly, the standard error estimates from the lsmeans statement are quite different. Take one example: mean (SE) for model-adjusted means for one visit of one treatment group: 9.78 (4.896) 9.78 (2.150) The adjusted means are the same but the standard errors change a lot; and the standard error values depend on the location/order of the covariates. I do not understand why. Am i doing something wrong or the variance-covariance changes with the order of the covariates? thanks all.
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