Hi NMUsers,
I am trying to model some data that contain a biomarker time course over 4
weeks
(with twice weekly measurements) and steady state of the change in biomarker is
attained in this duration in the presence of various doses of a drug. This is a
dose ranging experiment and I have value of exposure (AUC(0-tau)) at steady
state that drives the biomarker change. I noticed that there are some obvious
non-responders to the treatment (no mechanistic/physiological explanation).
When
using a direct effect Emax model the model fits are good and parameter
estimates
are well estimated. But the estimate of omega for EAUC50 is extremely high
(about 13) due to the very high values estimated for the non-responders, thus
simulations are extremely biased. I tried a mixture model on EAUC50 and NONMEM
estimated 15% non-responders with high EAUC50 values (while examining the
dataset non-responders are about 25%). With the mixture model implementation on
EACU50 the omega on EAUC50 dropped to about 4 which is again extremely high for
the responder population. I tried mixture model on Emax alone and EAUC50+Emax
together also but again got high omegas and lower than expected estimate of
non-responder population.
I would appreciate if I can get some ideas to take care of this issue. THANKS!!
Z Chen
Mixture modelling (responders vs non-responders)
Hi Zhong,
If you have clear non-responders, you might consider allowing
non-responders and responders to have different response functions
rather than different parameter estimates.
For the non-responders, response=constant (i.e., baseline) and for
responders, response=function(AUC(0-tau)).
Regards,
Colm
Quoted reply history
________________________________
From: [email protected] [mailto:[email protected]]
On Behalf Of Zhong Chen
Sent: Monday, July 19, 2010 6:00 PM
To: [email protected]
Subject: [NMusers] Mixture modelling (responders vs non-responders)
Hi NMUsers,
I am trying to model some data that contain a biomarker time course over
4 weeks (with twice weekly measurements) and steady state of the change
in biomarker is attained in this duration in the presence of various
doses of a drug. This is a dose ranging experiment and I have value of
exposure (AUC(0-tau)) at steady state that drives the biomarker change.
I noticed that there are some obvious non-responders to the treatment
(no mechanistic/physiological explanation). When using a direct effect
Emax model the model fits are good and parameter estimates are well
estimated. But the estimate of omega for EAUC50 is extremely high (about
13) due to the very high values estimated for the non-responders, thus
simulations are extremely biased. I tried a mixture model on EAUC50 and
NONMEM estimated 15% non-responders with high EAUC50 values (while
examining the dataset non-responders are about 25%). With the mixture
model implementation on EACU50 the omega on EAUC50 dropped to about 4
which is again extremely high for the responder population. I tried
mixture model on Emax alone and EAUC50+Emax together also but again got
high omegas and lower than expected estimate of non-responder
population.
I would appreciate if I can get some ideas to take care of this issue.
THANKS!!
Z Chen