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 <lgibian...@quantpharm.com>
*Sent:* Tuesday, September 27, 2016 5:49:00 PM
*To:* Sultan,Abdullah S; nmusers@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
