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

From: Justin Wilkins <justin.wilkins>
Date: Wed, 13 Feb 2019 09:56:12 +0000

Hi Sumeet,

For the VPC I would suggest (assuming you are using NONMEM) that you install Perl-speaks-NONMEM (PsN,, which can be used to run a VPC without a lot of additional coding. The results can easily be viewed using Xpose 4 ( Other tools like Monolix and nlmixr have VPC functionality baked in.

One would usually need to evaluate and balance all the diagnostic evidence available before coming to a conclusion. There’s no one magic plot or number, although how you weight the various diagnostics could depend on the purpose of the model (the question being asked). For instance, if you want PK predictions for driving a PD model, perhaps having good individual predictions of the subjects you have is more interesting than the model’s ability to predict new subjects (although being able to do so is still important). On the other hand, if you’re wanting to describe the PK at the population level or use the model for simulations, the population predictions and the residuals (and VPCs) are usually the most important things to look at – in this scenario individual fits, while still relevant, are not the primary objective. At the end of the day, though, all diagnostics should look OK unless something is wrong somewhere (with some rare exceptions, which you can check using mirror plots in PsN and Xpose).

In addition to the diagnostics Nick and Leonid have mentioned, how precisely estimated are your parameters? What is the estimated shrinkage on your random-effects parameters? How big is your condition number (ratio of lowest and highest eigenvalues)? Together with the other diagnostics, these snippets of information can also add to the overall picture.


Justin Wilkins, PhD
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59457 Werl
+49 2922 927 8843
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From: owner-nmusers
Sent: 13 February 2019 09:34
To: Singla, Sumeet K <sumeet-singla
Cc: nmusers
Subject: Re: [NMusers] OFV or Diagnostic Plot ?? Which one rules...

DV vs IPRED is only one, and the least helpful plot. You may want to look on DV vs PRED, both in original scale and on log log scale, CWRES vs time, PRED, distributions and correlation of random effects, etc. and only then one can decide which of the models is better. Based on the description, I would guess that model with proportional error provides better fit at very low concentrations, visible in log scale plots. So you may also factor this in in the decision process. If max concentrations are more important, additive error may help but if low concentrations are more important, you may want to use combined or proportional error.

On Feb 13, 2019, at 7:28 PM, Singla, Sumeet K <sumeet-singla
Hi Everyone,

I am fitting two compartment PK model to Marijuana (THC) concentrations. When I apply proportional error (or proportional plus additive) residual model, 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 fits in all subjects but objective functional value is increased by about 20 units. DV vs IPRED reveal all concentrations on line of unity.
My question is: should I go with additive error model which gives me perfect fit but higher OFV or should I go with proportional error model which gives 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 - 04:56:12 EST

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