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From: Jakob Ribbing <jakob.ribbing_at_pharmetheus.com>

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 =

dramatically.

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*:

TVCL=THETA(1)*(WT/70)**THETA(2)

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

Jakob

*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_at_uiowa.edu> =
*

wrote:

*>
*

*> 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_at_uiowa.edu <mailto:sumeet-singla_at_uiowa.edu>
*

*> 518.577.5881
*

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

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 =

dramatically.

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*:

TVCL=THETA(1)*(WT/70)**THETA(2)

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

Jakob

*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.

wrote:

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?

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