NONMEM Users Network Archive

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RE: OMEGA selection

From: Bill Bachman <bachmanw>
Date: Wed, 15 Apr 2009 13:45:45 -0400

In my opinion, I would not remove those in the 10-90% range. I would be
suspect of anything over 100%, even with noisy data, they are being poorly
estimated.

 

  _____

From: owner-nmusers
Behalf Of Ethan Wu
Sent: Wednesday, April 15, 2009 1:15 PM
To: Bachman, William; nmusers
Subject: Re: [NMusers] OMEGA selection

 

some Etas estimated to be around 2 or 3, but since I am fitting a quite
noisy PD data, I think they are actually reasonable

no Etas close to 0

cov% esimated in the range of 10-90%.should those small ones like 10% be
taken out?


 

  _____

From: Bachman, William <William.Bachman
To: Ethan Wu <ethan.wu75
Sent: Wednesday, April 15, 2009 12:12:56 PM
Subject: RE: [NMusers] OMEGA selection

Well, the first thing that I would do is look at the magnitude of the
estimates of the etas. I would eliminate those etas that are poorly
estimated (essentially the very large values or those approaching zero).

 

  _____

From: owner-nmusers
Behalf Of Ethan Wu
Sent: Wednesday, April 15, 2009 11:47 AM
To: nmusers
Subject: [NMusers] OMEGA selection

Dear all,

  I am fitting a PD response, and the equation goes like this:

total response = baseline+f(placebo response) +f(drug response)

first, I tried full omega block, and model was able to converge, but $COV
stop failed.

To me, this indicates that too many parameters in the model. The structure
model is rather simple one, so I think probably too many Etas.

I wonder is there a good principle of Eta reduction that I could implement
here. Any good reference?

 

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Received on Wed Apr 15 2009 - 13:45:45 EDT

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