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RE: OFV or Diagnostic Plot ?? Which one rules...

From: Eleveld-Ufkes, DJ <d.j.eleveld>
Date: Wed, 13 Feb 2019 10:06:49 +0000

Hi Sumeet,

OFV is an objective fit of the model to the data, so generally that should =
be your leading criteria. Outside of that you often have to deal with subje=

However, there are quite a few caveats to relying too strongly on OFV. You =
should try to avoid "chasing OFV" by testing too many models or those in wh=
ich the theoretical justification is lacking. Which error model best agrees=
 with other information you have about the concentrations you have?

You might also consider whether poor diagnostics you see as part of the res=
idual error model really originate from structural model-misspecification. =
It can happen that you "hide" the shortcomings of the structural model by p=
utting too much flexibility into the residual error model. It then becomes =
very hard to improve the structural model when the information is "swallowe=
d up" by the residual error model. You cant fix what you cant see.

I often try to think about how the model will be used outside of the develo=
pment process. In its intended application does the model need to predict h=
igh concentrations or low concentrations more accurately? A proportional er=
ror model lets low concentrations play a stronger role in the model likelih=
ood compared to proportional+additive. Basically, getting OFV 20 points low=
er with prop+add compared to prop means that the model can fit higher conce=
ntrations better if a little bit worse fit can be tolerated in low concentr=
ations. You have to decide which one is most appropriate. It depends on how=
 the model is intended to be used and how your structural model compares to=
 what you think the "true" model might be.

Warm regards,

Douglas Eleveld

From: owner-nmusers
 Behalf Of Singla, Sumeet K
Sent: woensdag 13 februari 2019 07:28
To: nmusers
Subject: [NMusers] OFV or Diagnostic Plot ?? Which one rules...

Hi Everyone,

I am fitting two compartment PK model to Marijuana (THC) concentrations. Wh=
en I apply proportional error (or proportional plus additive) residual mode=
l, I get pretty good fits (except 15% of subjects) at all time points.
However, when I apply only additive error residual model, I get perfect fit=
s in all subjects but objective functional value is increased by about 20 u=
nits. DV vs IPRED reveal all concentrations on line of unity.
My question is: should I go with additive error model which gives me perfec=
t fit but higher OFV or should I go with proportional error model which giv=
es me lower OFV but not so good fit in couple of subjects?

Sumeet Singla
Graduate Student
Dpt. of Pharmaceutics & Translational Therapeutics
College of Pharmacy- University of Iowa

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Received on Wed Feb 13 2019 - 05:06:49 EST

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