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PROC NLMIXED random effects model produces estimates that are too low.

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PROC NLMIXED random effects model produces estimates that are too low.

I am using PROC NLMIXED to produce a poisson mixed model (I'm using this method instead of GLIMMIX or GENMOD because I need the correct confidence intervals for the Pre/Post difference in means).   The first model is the fixed effects model, the second model is the mixed model:

FIXED EFFECTS MODEL (matches results from GLIMMIX, GENMOD)

PROC NLMIXED data=prepost;

PARMS b0=-1.6 b1=0.1;

eta = b0 + b1*prepost2;

lambda=exp(eta);

model countRx=poisson(lambda);

estimate 'pre level ' exp(b0);

estimate 'post level' exp(b0 + b1);

estimate 'pre-post difference' exp(b0+b1)-exp(b0);

run;

RANDOM EFFECTS MODEL (to adjust for correct pre post estimates)

PROC NLMIXED data=prepost;

PARMS b0=-1.6 b1=0.1 s2u=0;

eta = b0 + b1*prepost2 + u;

lambda=exp(eta);

model countRx=poisson(lambda);

random u~normal(0, s2u) subject=enrolid

estimate 'pre level ' exp(b0);

estimate 'post level' exp(b0 + b1);

estimate 'pre-post difference' exp(b0+b1)-exp(b0);

run;

Here's the problem:

The fixed effects model gives these estimates:

parameter estimates

bo  -1.5994

b1  0.09337

estimates

period   countRx

pre       0.2020

post      0.2218

pre-post  0.01977

But the random effects model gives these estimates:

parameter estimates

bo   -6.4813

b1    0.09337

s2u   26.2858

estimates

period   countRx

pre       0.001532

post     0.001682

pre-post  0.000150

We would expect some changes because of the switch from independent measures to a repeated measures model, but nothing like this.   What is the problem?    Is the model specification correct?

Ron Levine

Message was edited by: Ronald Levine:

Since I've been advised the the above code is correct for the Poisson with random effects model, there is another issue at hand:  the large random effects variance (s2u=26) may be diminishing the mean estimation.   (I tried the same model using PROC GLIMMIX and the estimation was not affected.   Why? )    My question to the community is:   Should we use a NegBin distribution instead of Poisson in PROC NLMIXED?    Or, (since 92% of the count outcomes are zeros), should we use a ZIP or ZINB distribution instead?    How would these alternative models be coded in PROC NLMIXED? Ron Levine

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Posts: 2,655

Re: PROC NLMIXED random effects model produces estimates that are too low.

Hi Ron,

I would definitely consider ZIP or ZINB with that many zeroes.  A good place to search for code to do this is in the SAS-L archives.  Look for posts by Dale McLerran.  There is an especially good one dated 14 April 2003.

Steve Denham

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