I think we are making it more difficult than needed, especially for the
people who just started using the NLME. It does not hurt to include
statistically significant covariate in the model even if the actual
effect is small and does no manifest itself on the standard diagnostic
plots.
It make sense to check whether there is an error in the model code.
Plots of random effects versus covariates of interest should help to see
whether covariate model changed the individual random effects. If not
(that is, random effects of the model with and without covariate effect
are numerically identical) then the coding is wrong and should be checked.
Thanks
Leonid
On 10/29/2019 11:00 AM, Luann Phillips wrote:
Hi,
If _all_ of the individual predictions are the same for the model with
the covariate and without the covariate, then it sounds like the
original model is at a local minimum instead of a global minimum.
Best regards,
Luann
*From:* owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> *On
Behalf Of *Singla, Sumeet K
*Sent:* Tuesday, October 29, 2019 10:00 AM
*To:* nmusers@globomaxnm.com
*Subject:* [NMusers] Stepwise covariate modeling
Hi!
I am performing stepwise covariate modeling using PsN feature in Pirana.
I am getting some covariates which are statistically reducing OFV
significantly, however, when I include those covariates in the PK model,
the results I am getting are exactly similar to what I am getting in my
base model, i.e. there is no difference in individual predictions or pop
predictions or any other diagnostic plots. So, does that mean I should
move forward WITHOUT including those covariates as they don’t seem to be
explaining inter-individual variability despite scm telling me that they
are statistically significant?
Regards,
*Sumeet K. Singla*
*Ph.D. Candidate*
*Division of Pharmaceutics and Translational Therapeutics*
*College of Pharmacy | University of Iowa*
*Iowa City, Iowa*
*sumeet-sin...@uiowa.edu <mailto:sumeet-sin...@uiowa.edu>*
*518.577.5881*
