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Re: Stepwise covariate modeling

From: Jakob Ribbing <jakob.ribbing>
Date: Tue, 29 Oct 2019 15:46:51 +0100

Hi Sumeet,

If you have rich sampling (and rich information on all parameters of =
interest) then one would not expect much difference between the =
individual parameter estimates with/without covariates in the model.
This does not make the covariate model meaningless, since future =
patients may be sparsely sampled, or the model may be used to identify =
subpopulations, or for predictions of future patients, etc.

When you say that pop predictions do not change, exactly what do you =
mean by that? The population typical value is not expected to change =
much (it may for categorical covariates with high impact) - the =
interpretation of the population parameter value has shifted from e.g. =
"median parameter value in population" (base model) to "median parameter =
value for a subject with typical covariate values".
This is because the covariate equations are generally centered around =
typical covariate values: We do not want the population parameter to =
represent CL for a subject with zero kilo body weight - had it been =
coded that way the population parameters would have changed =

So the question is rather if the typical parameter values for two =
subjects with different covariate values are different to a degree that =
it is important to account for (i.e. clinically relevant).
If we assume that you have body weight on CL, you can calculate e.g. the =
2.5th and 97.5th percentiles of body weight in your population (or in =
another population or relevance).
And then you can calculate TVCL for these two different weights and =
compare to the typical body weight (e.g. 70 kg).
You may have for example this equation*:

Based on the point estimate and SE of THETA 2, you can then calculate =
percent change (from the typical 70 kg body weight) with point estimate =
and 95% CI, for each of the two extreme body weights.
And you can illustrate this in a so-called Forest plot (or tornado =
plot), for all covariate coefficients.

If the CI is wide, the data does not contain enough information to rule =
out clinical relevance (if you think the parameter in question is =
important - maybe abs rate is in some cases not, for examples).
But given that it has been selected by SCM, if the SE agrees (with LRT) =
CIs should not overlap with zero percent change.

If the CI is tight and with small change in the parameter, then that =
covariate relation can be concluded to be clinically irrelevant, despite =
being statistically significant. This may happen if you have many =
subjects in your data.
(Or if your limit for what is a relevant change is very wide)
In this case it may be justified leaving that covariate relation out of =
the final model.

Then of course, the fact that something was not statistically =
significant does not mean that the covariate effect is clinically =
irrelevant - it may just be that you do not have enough information.
To assess that you would need to use FREM or FFEM (instead of SCM) - but =
this is out of scope for your original question.

Best wishes


*actually, for this example, THETA 2 may be fixed according to =
allometric principles, but let’s assume this is a large molecule =
and that allometry was not deemed suitable in this case, and therefore =
the covariate was tested in SCM, or otherwise estimated.

> On 29 Oct 2019, at 15:00, Singla, Sumeet K <sumeet-singla
> 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-singla
> 518.577.5881

Received on Tue Oct 29 2019 - 10:46:51 EDT

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