Re: Coding delayed covariate effects
Dear Elisabet,
The most complete way to handle your problem would be to treat the
covariates as observations and build a separate model for them. Your
data set would change from wide to long format. So instead of having
ID TIME DV CYTC
1 0 X1 Y1
1 1 X2 Y2
you would have
ID TIME DV TYPE
1 0 X1 1
1 0 Y1 2
1 1 X2 1
1 1 Y2 2
where TYPE is a flag specifying the type of measurement.
In your model file you use the TYPE flag to return the prediction
corresponding to the current row i.e.,
IF(TYPE.EQ.1) Y=..... ;model prediction for concentration
IF(TYPE.EQ.2) Y=.... ; model prediction for covariate
This would increase the modeling effort considerably but has the
advantage that you can now use any model to link your covariates and
clearance. One option for a delay between change in covariate and
clearance would be an indirect response model. Another advantage of
this approach, is that you acknowledge the uncertainty in the
covariate (since it is just another type of measurement).
Concerning your second question, you could have a look at prediction
corrected VPCs as described by Bergstrand et al.:
Bergstrand, Martin, Andrew C Hooker, Johan E Wallin, and Mats O Karlsson.
“Prediction-corrected Visual Predictive Checks for Diagnosing
Nonlinear Mixed-effects Models.”
The AAPS Journal 13, no. 2 (June 2011): 143–151.
I hope that helps!
Best regards,
Sebastian
Sebastian Ueckert, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences,
Uppsala University
-----------------------------------------------
P.O. Box 591
SE-751 24 Uppsala
Sweden
-----------------------------------------------
[email protected]
-----------------------------------------------
Work: +46-(0)18-471 4437
Quoted reply history
On Thu, Feb 23, 2012 at 2:50 PM, Elisabet Størset
<[email protected]> wrote:
> Dear nonmemusers
>
>
> In my PK modeling project, a drug (elimination: 100 % hepatic metabolism) is
> given orally twice daily over a period of 10 weeks. 100 subjects contribute
> daily trough concentrations. During this period, covariates vary within
> subjects. Two covariates that are difficult to handle are changes in
> corticosteroid regime and changes in cytokine concentrations, which both are
> expected to alter metabolism (CYP enzymes) at a transcriptional level. This
> means that a covariate effect on CL is expected to be delayed and/or last
> for some time (unknown for how long).
>
>
> My first strategy is to code this in the normal way:
>
> TVCL=THETA(1) + THETA(2) *(cytokine concentration) or (corticosteroid dose)
>
>
> However, when coding the covariate as continuous with this method, it will
> not take into account that an effect does still have an impact on the
> parameter after the covariate value has returned to normal in the data set.
>
>
> Example 1 - Low dose steroids are typically administered daily. Then, high
> dose methylprednisolone is given I.V. for 3 days, before going back to the
> low dose. The inductive effect on CYP enzymes from the high dose could be
> delayed and then last for 1-2 weeks even though the high dose is not
> maintained.
>
> Example 2 - Cytokines marks an inflammation, which could have temporary
> impact on CL/F. Cytokine concentration returns to normal, but the impact on
> the parameter could be lasting for some days.
>
>
> Does anyone have experience of a strategy to code these kinds of covariate
> relationships in NONMEM? Could NONMEM estimate for how long time CL/F might
> be affected, or is this too much to ask for? :)
>
>
>
> Additional question: Is there any way to make the visual predictive check an
> appropriate validation method when only trough concentrations are known over
> a long period of time, when doses are frequently changing within and between
> subjects during the period?
>
>
> Thank you all in advance for your kind help :)
>
>
> Elisabet, pharmacy student, University of Bergen