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Re: [NMusers] residual variability

From: Leonid Gibiansky <lgibiansky_at_quantpharm.com>
Date: Thu, 29 Sep 2016 09:30:02 -0400

What I meant was that after you remove the random effect on the lag time
(and stabilize the model) you may introduce inter-individual variability
on delay by using transit compartment with random effect or zero-order
absorption with random effect on duration of infusion (followed by the
first-order).
Leonid


On 9/29/2016 12:53 AM, Sultan,Abdullah S wrote:
> Hi Dr. Gibiansky
>
>
> Thanks, removing the random effect on the lag time help stabilize the model.
>
>
> I used a transit compartment and sequential and it did not help, I still
> get very large parameter estimates.
>
>
> I am using Monolix for the modeling
>
>
> Thanks,
>
> Abdullah
>
>
> ------------------------------------------------------------------------
> *From:* Leonid Gibiansky <lgibiansky_at_quantpharm.com>
> *Sent:* Tuesday, September 27, 2016 5:49:00 PM
> *To:* Sultan,Abdullah S; nmusers_at_globomaxnm.com
> *Subject:* Re: [NMusers] residual variability
>
> Abdullah,
> Do you have random effect on the lag time? Models with random effects on
> the lag time are very difficult to work with, try to remove the lag and
> use the transit compartment(s) to describe the delay. Make sure you have
> INTERACTION option on the estimation step, use METHOD=1. Sometimes
> models with sequential 0-order and 1-st order absorption describe delay
> better (with estimated D1 of infusion to the depot compartment).
> Leonid
>
>
>
>
>
> On 9/27/2016 1:12 PM, Sultan,Abdullah S wrote:
>> Hi everyone,
>>
>>
>> I have a rich data set for a drug administered orally. The drug has slow
>> absorption (Tmax 4 hours) and rapid elimination (2 hours half life). A
>> tlag model was sufficient to describe the data but I ran
>> into difficulties with the error model.
>>
>>
>> If I use a proportional or combined error model, the model is unstable
>> and I get unrealistic estimates (very large Vd, Cl and residual
>> variability) . It is only stable if:
>>
>> 1) I use a constant error model
>>
>> 2) Use a combined error model and fix the a part
>>
>>
>> When I use a constant error model, the diagnostic plots clearly show the
>> error is not constant
>>
>>
>> Not sure what the cause for this is, I tried several things to fix it
>> like changing initial estimates or structural model (transit
>> compartment, zero order,....), deleting outliers or low concentrations
>> near the BLQ but the problem still persists.
>>
>>
>> Any suggestions
>>
>>
>> Thanks,
>>
>> Abdullah Sultan
>>
Received on Thu Sep 29 2016 - 09:30:02 EDT

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