This month, I will talk about about a new iteration of mu-referencing in nlmixr2, which I call mu3 and mu4. What is mu referencing in nlmixr2 – Review from another post From the last blog post about mu-referencing, I will give a brief overview of mu-referencing and what mu-2 referencing is and how it is expanded a bit more. mu-referencing is combining a fixed effect, random effect and possibly a covariate in the form:
One of the things that can be useful from time to time is to calculate different PK parameters based on whatever parameters you have estimated. There is a great function, calc_derived() in pmxTools that allows this calculation of the derived parameters (by my collaborators Justin Wilkins and Bill Denney). I think this is an underrated function that can help many people with typical calculations. rxode2 has the same type of function, which can be helpful to test the linCmt() functions, rxDerived().
This month, I will talk about about a new iteration of mu-referencing in nlmixr2, which I call mu2. What is mu referencing in nlmixr2 mu-referencing is combining a fixed effect, random effect and possibly a covariate in the form: [ \theta_\mathsf{pop}+\eta_\mathsf{individual}+\theta_\mathsf{covariate}\times \mathsf{DataCovariate} ] Often they are placed in exponentials for these to be log-normally distributed like: [ \exp\left(\theta_\mathsf{pop}+\eta_\mathsf{individual}+\theta_\mathsf{covariate}\times \mathsf{DataCovariate}\right) ] In optimization routines like saem, these are switched out with a single parameter during optimization classically called (\phi) in both NONMEM and Monolix.