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
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
Quoted reply history
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
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
Quoted reply history
________________________________
From: Leonid Gibiansky <[email protected]>
Sent: Tuesday, September 27, 2016 5:49:00 PM
To: Sultan,Abdullah S; [email protected]
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
>
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
Quoted reply history
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 <[email protected]>
> *Sent:* Tuesday, September 27, 2016 5:49:00 PM
> *To:* Sultan,Abdullah S; [email protected]
> *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