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Posted 10-29-2012 04:20 PM
(2104 views)

Hi all,

I am using PROC GLIMMIX to fit a 3-level model (clustered by hospital and then by physician, for example).

SAS automatically computes the overall covariance parameter estimate for hospital(physician).

How can I obtain the individual ones? i.e. just by hospital and just by physician? (this is asked of me to justify the use of clustering modelling techniques)

Thanks in advance!

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The parameter for hospital should not be difficult--just adding it to the RANDOM statement should give a value. The request for "just by physician" kind of begs the question--if the physicians are truly nested in hospital, what does this mean? The only approach I can think of is to fit the data without hospital as a random effect. Since the fixed effects are identical, you could look at the Information Criterion of your choice (AIC, AICc, etc.) as a guide as to whether the model fits your data "better". So, I see three runs using the following RANDOM statements:

1. Just hospital

RANDOM intercept/subject=hospital:

2. Just physician

RANDOM intercept/subject=physician;

3. Nested

RANDOM intercept hospital/subject=physician;

Since these are all subject-based, I would start with adaptive quadrature (METHOD=QUAD) as the estimation method.

Steve Denham

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The parameter for hospital should not be difficult--just adding it to the RANDOM statement should give a value. The request for "just by physician" kind of begs the question--if the physicians are truly nested in hospital, what does this mean? The only approach I can think of is to fit the data without hospital as a random effect. Since the fixed effects are identical, you could look at the Information Criterion of your choice (AIC, AICc, etc.) as a guide as to whether the model fits your data "better". So, I see three runs using the following RANDOM statements:

1. Just hospital

RANDOM intercept/subject=hospital:

2. Just physician

RANDOM intercept/subject=physician;

3. Nested

RANDOM intercept hospital/subject=physician;

Since these are all subject-based, I would start with adaptive quadrature (METHOD=QUAD) as the estimation method.

Steve Denham

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Hi Steve. (love your work by the way)

Is it possible that some physicians are observed at more than one hospital in hypermonkey's data? In some health systems this is fairly common within a region or city, or if, for example, some specialists are hard to find.

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If that is the case (physicians not truly nested), then two RANDOM statements will be needed, each with a different subject. Ordering the RANDOM statements then becomes critical as well. I would go with:

4. Physicians not nested

RANDOM intercept/subject=hospital;

RANDOM intercept/subject=physician;

If METHOD=MSPL is adequate (rather than adaptive quadrature), these can then be combined into a single, non-subject defined, statement:

RANDOM hospital physician;

Steve Denham

Message was edited by: Steve Denham, who can't spell this morning

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Hi Damien,

Interesting question! Not a problem in this particular case, but definitely something to consider. Thanks!

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Hi Steve,

This seems to be exactly what I am looking for. The estimates in case 3 (nested) will help justify why a clustering modelling approach is necessary for this analysis.

Many thanks!

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I'll take a "Correct Answer" for that then

Steve Denham

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