Jump to content


Photo

Why does NLME set a diagonal omega matrix as the default?

NLME Omega Diagonal Block Full Block

  • Please log in to reply
2 replies to this topic

#1 dan.hines

dan.hines

    Newbie

  • Members
  • Pip
  • 4 posts

Posted 25 October 2018 - 05:16 PM

I am curious as to why NLME sets a diagonal omega matrix instead of a full block omega matrix as the default when first attempting to converge a population PK model. Wouldn't it be best to first attempt converging a model that has a full block omega matrix (aka off diagonal covariances estimated as well as variances) and then check the estimated covariance values to confirm that there is negligible (say less than 0.0025) instead of setting a diagonal omega matrix from the start? 

 

Dan 



#2 Simon Davis

Simon Davis

    Advanced Member

  • Administrators
  • 1,116 posts

Posted 26 October 2018 - 08:56 AM

Hi Dan,

      I guess there are different approaches that people take. I think the general design of the Phoenix model object was to guide newer users try to follow the general Rule of Parsimony, i.e. Choose the simplest model that fits the data and addresses questions of interest. 

 

   So yes it's an assumption that by defaulting a diagonal omega matrix we are suggesting all random effects independent but if the model fits the data well enough then that means less parameters to be adjusted so a simpler model.  As you note it's easy enough to change and test subsequently.

   Simon.


  • dan.hines likes this

#3 smouksassi1

smouksassi1

    Advanced Member

  • Members
  • PipPipPip
  • 169 posts
  • LocationMontreal

Posted 26 October 2018 - 01:10 PM

I would speculate also  that this is the default for historical reasons  and because the default engine is FOCE-ELS which might have issues fitting full omegas and or run times can be very long.

 

Depending on your situation I would indeed go into full omega and a more modern EM based engine to start with.

Indeed we always strive for a parsimonious model but keep in mind the intended use of your model.

Reduce your model but not beyond the intended use of it

 

If your data does not support a full omega and you want to simulate from your model you might end up simulating un-realistic combination of the independent etas. Better make a strong assumption about a value correlating Vc/CL for example.


  • dan.hines likes this





Also tagged with one or more of these keywords: NLME, Omega, Diagonal Block, Full Block

0 user(s) are reading this topic

0 members, 0 guests, 0 anonymous users