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

Hi Sumeet, For the VPC I would suggest (assuming you are using NONMEM) that you install Perl-speaks-NONMEM (PsN, https://uupharmacometrics.github.io/PsN/index.html), which can be used to run a VPC without a lot of additional coding. The results can easily be viewed using Xpose 4 ( https://cran.r-project.org/package=xpose4). 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. Best Justin — Justin Wilkins, PhD Occams Kirchnerstraße 22 59457 Werl Germany www.occams.com +49 2922 927 8843 [email protected]<mailto:[email protected]> https://www.linkedin.com/in/justinjwilkins/ [cid:[email protected]]