About jiltao

jiltao · ‎08-27-2021

What happens if you add the singular=1e-8 option in the MODEL statement in PROC MIXED? If that does not help, then I would need to have your data set to look into it further, as this type of issue is often data/model dependent. Thanks, Jill

jiltao · ‎08-26-2021

Sorry I did not realize that you are a different person 🙂 Please note that Steve's code computes the difference in T2 (the response) between the groups, not the difference between T2 and T0. You said that his approach answers your question, but you want the difference between T2 and T0 -- that does not make sense to me. Maybe I still miss your request. Jill

jiltao · ‎08-26-2021

Your PROC GLM code treats the baseline measurements (T0) as a covariate, and I am not sure if a percent changes between the measurements at T2 and T0 can be computed based on this model specification. Steve's PROC MIXED model specification computes the difference between the groups in T2, accounting for the baseline measurements. Is that what you wanted? If you want to compute the difference between T2 and T0, whether it is a percent difference or the raw difference, you would probably need to arrange your data to have a TIME variable in there, just like what you sent in the original post. I think my PROC NLMIXED code worked for your original question, right? That model has TIME and TREATMENT (or GROUP?) in there. How is your new question different from the previous one in terms of the data and model? Thanks, Jill

jiltao · ‎08-25-2021

My understanding is that, you wanted the percent change, also wanted the significance of the percent change (p-value, or 95%ci, or both), correct? If so, you would need to use PROC NLMIXED to fit your model and use the ESTIMATE statement to get what you want. The syntax of the ESTIMATE statement depends on your model set up. For this new request, can you send me your current program (proc glm or proc mixed) and exactly what percent change you want? I will see if I can come up with the PROC NLMIXED program for you. Thanks, Jill

jiltao · ‎08-20-2021

The COVTEST result tells you that the variances are different among the treatment groups. So your RANDOM _RESIDUAL_ / GROUP=TREATMENT; statement is necessary, because it fits an unequal variance model to your data. You do not need to do data transformations. Constant variance is never one of the model assumptions in PROC MIXED / PROC GLIMMIX. You just model it, which is what your RANDOM _RESIDUAL_ statement does. Hope this helps, Jill

jiltao · ‎08-04-2021

The PROC MIXED documentation below has detailed information on how n and d are determined for various situations -- https://go.documentation.sas.com/doc/en/pgmsascdc/v_014/statug/statug_mixed_syntax01.htm#statug.mixed.procstmt_ic So, n equals the number of effective subjects as displayed in the "Dimensions" table, unless this value equals 1, in which case n equals the number of levels of the first random effect you specify in a RANDOM statement. In your case, n=4 for BIC. For restricted likelihood estimation, d equals q, the effective number of estimated covariance parameters. In your case, d=11 for the UN model and d=2 for the VC model. The BIC value for model 1 = -2l + d*logn = 17.0 + 11*log(4) = 32.2 The BIC value for model 2 = -2l + d*logn = 27.1 + 2*log(4) = 29.9 They are consistent with PROC MIXED output. As far as "inconsistency" between AICC and BIC -- I do not compare these two fit statistics among themselves. I compare the same fit statistics (AICC or BIC) across different models. And I assume that is what these fit statistics are used. I am not sure what other sources you read about BIC. I am personally not aware of a reference where n and d are defined explicitly for models with RANDOM and REPEATED statements for all these fit statistics. But it could just be my ignorance. Our developers did their research and came up with these calculations for various data and modeling situations. If you have good references for different computations please share it with me, I would appreciate that. Thanks, Jill

jiltao · ‎08-03-2021

Assuming 5% is in the unit of average score (not 5% of the mean, for example), your code looks reasonable to me.

jiltao · ‎07-30-2021

the LOCAL= option is not available in the RANDOM statement in PROC GLIMMIX, so you cannot fit the same model in PROC GLIMMIX, unfortunately. I personally think the LOCAL=EXP() option might be a too strong assumption in some cases, and you might consider using the type=csh residual option in the RANDOM statement in PROC GLIMMIX to fit something similar.

jiltao · ‎07-22-2021

Independent errors is never one of the model assumptions for mixed models. Normal distribution with the variance covariance matrix of R is one of the model assumptions. You would use the REPEATED statement in PROC MIXED to model the correlated residuals. Or, you could use the RANDOM statement to model the the correlated observations in the response. If you do a residual analysis in PROC MIXED, how non-normal does it look like? One way is to use PROC GLIMMIX to model your data if a different distribution (from the exponential family) is more appropriate.

jiltao · ‎07-20-2021

So you have repeated measures for each patient, and patients are nested within hospitals? If so, you might want to use the following RANDOM statements in PROC GLIMMIX -- random int / subject=hospital; random int / subject=patient_ID(hospital); If the model is still too large, please let me know. There might be ways to make the model less resource intensive if needed.

jiltao · ‎07-01-2021

Sometimes the default singularity criterion might be too strict and relaxing the criterion might help. This can be the case when the message you get is "Warning: Stopped because of infinite likelihood". You need to be careful not to relax the criterion too much though.

jiltao · ‎07-01-2021

What happens if you add the singular=1e-10 option in the MODEL statement in PROC MIXED? if it does not help, I would go with the UN or TOEP structure (depending on which one fits your data better) using all data. If some covariance structures do not give you a convergence, maybe those structures are not appropriate for your data.

jiltao · ‎06-25-2021

Are Treatment and Black continuous (or dummy) variables or are they in the CLASS statement? Can you send us your complete PROC MIXED code and the Class Level Information table from PROC MIXED output?

jiltao · ‎06-24-2021

Can you send me the Log and Output from this new attempt?

jiltao · ‎06-14-2021

I do not think this SLICE statement works. The SLICEBY= syntax is not appropriate.

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