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PopPK with Nested Random Effects


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#1 djtaylor

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Posted 07 December 2022 - 02:13 PM

Hi -

 

I am trying to fit a Pop PK model for repeat extravascular dosing and need to specify a within-subject random absorption effect (i.e., absorption varies both across subjects and within subjects over time). I'm doing this with the textual editor because of additional complexities (there's both an immediate and delayed absorption component). My question is: how do I specify nested random effects (or can it even be done using Phoenix NLME)?

 

Thanks,

 

Doug Taylor   



#2 bwendt@certara.com

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Posted 09 December 2022 - 01:46 PM

Hi Doug,

 

perhaps you can share more details of the data that you want to fit? 

As a first response, if you have within-subject effects I would suggest you try inter-occasion variable where you can label each individual dosing event with a different id. I am not sure why you would need a nested random effect, perhaps you mean a correlation between two random effects. This is typically setup in the omega matrix where you can ask for estimation of off-diagonal elements.

Hope, this helps for a start, we might give more guidance when we have more details.

 

Bernd



#3 djtaylor

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Posted 09 December 2022 - 02:04 PM

Thanks for getting back to me. The setup is as follows:

 

Subjects receive subcutaneous injections of a drug every 6 months (with flip-flop kinetics). The absorption rate (and clearance, etc.) varies across subjects. However, because absorption is affected by where each injection is given (e.g., depth of injection, vascularization of injection site, etc.), there is within-subject (occasion to occasion) variability in absorption. The random-effects model for absorption is then: Ka_ij = a_i + a_ij , where a_i is an across subjects random effect and a_ij is a (nested within subject) random effect for dosing event j within subject i.

 

From what I can tell about Phoenix NLME (and I am a relatively new user!), the omega matrix does not readily permit such nested effects. As you mention, I could let ID correspond to unique dosing events, but I would need drug clearance (as an example) not to vary by dosing event but by subject, so that coding would open up another problem. 

 

 

Hi Doug,

 

perhaps you can share more details of the data that you want to fit? 

As a first response, if you have within-subject effects I would suggest you try inter-occasion variable where you can label each individual dosing event with a different id. I am not sure why you would need a nested random effect, perhaps you mean a correlation between two random effects. This is typically setup in the omega matrix where you can ask for estimation of off-diagonal elements.

Hope, this helps for a start, we might give more guidance when we have more details.

 

Bernd



#4 bwendt@certara.com

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Posted 09 December 2022 - 03:58 PM

Doug, 

 

it seems to me that the occasion random effect is what you are looking for. We can help you to incorporate into your model, but we would need your Phoenix project file to do that. If this is confidential, you may want to share it through our support system, e.g. sending a message to support@certara.com

 

Bernd



#5 djtaylor

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Posted 09 December 2022 - 05:12 PM

Bernd,

 

Thanks for getting me pointed in a direction re: IOV. I see an example project called "Midazolam_Circadian_IOV_TwoEpsilon_Estimation_VPC.phxproj" which I can try and work with. However, 'occasion' does not appear to be 'nested' within subject in that example (there are no repeated 'occasions' within subject). But I'm probably missing something!

 

Doug


 

 

Doug, 

 

it seems to me that the occasion random effect is what you are looking for. We can help you to incorporate into your model, but we would need your Phoenix project file to do that. If this is confidential, you may want to share it through our support system, e.g. sending a message to support@certara.com

 

Bernd



#6 bwendt@certara.com

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Posted 12 December 2022 - 09:12 AM

Doug,

 

the occasions would need to be coded into your input, e.g. you could add a dosing ID (new column) that distinguishes one dose interval from another.

 

Bernd






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