RE: Reducing ETAs actually decreased OFV
Hi Xinting,
This is a rather broad (and often highly-opinionated) topic. At the highest
level, you can only fit parameters in a model where you have enough data to
estimate the parameter. A simple example is that if you have data that you
want to fit an Emax model to with measurements only up to the EC10, you don't
have enough data to estimate Emax and ED50; it will look fully linear.
Due to data variability and the fact that Q and V3 are less correlated with the
measurements than other parameters (Ka, V2, and CL have a stronger effect on
the measurements than Q and V3), the estimates will be more difficult. A good
example of how to evaluate this would be what Peter Bonate just suggested: do
likelihood profiling on each of the parameters (especially the ETAs) to
estimate the certainty (peakedness) or uncertainty (flatness) in the parameter
estimates.
Thanks,
Bill
Quoted reply history
From: Xinting Wang [mailto:[email protected]]
Sent: Monday, August 26, 2013 9:27 AM
To: Leonid Gibiansky
Cc: [email protected]; Denney, William S.
Subject: Re: [NMusers] Reducing ETAs actually decreased OFV
Dear Bill,
Appreciate your reply a lot. The issue is from KA. Adding KA or not did have
this problem. However, regarding your statement "it is rare to have enough data
to fit true IIV", can you explain more about this. My data set is from Phase I
studies, and I thought this should be enough for this simulation.
Dear Leonid,
Thanks very much for your detailed suggestion. I followed the steps you listed
above, and did find that the OFV decreased in step 2, just as you predicted.
Then using the estimation to replace the initial values for all of the THETA,
OMEGA and SIGMA, the OFV stabilized. However, I am curious about the
explanation for this. And, is this a universal method for estimation of initial
values? Thank you.
The change of OFV was around 100 (the OFV was ~114300). I am pasting the OMEGA
matrix below for your information.
ETA1 ETA2 ETA3 ETA4 ETA5
ETA1
+ 4.08E-02
ETA2
+ 0.00E+00 1.57E-01
ETA3
+ 0.00E+00 0.00E+00 1.30E-01
ETA4
+ 0.00E+00 0.00E+00 0.00E+00 4.07E-01
ETA5
+ 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.19E-02
ETA5 (0.0219) is the one caused the problem.
Best Regards
On 26 August 2013 07:06, Leonid Gibiansky
<[email protected]<mailto:[email protected]>> wrote:
Hi Xinting,
You should be able to do it. Let's check it again this way
1. You run the model with all ETAs included, but one ETA (the one that was
excluded in the reduced model) is fixed to zero. You should be able to
reproduce your "reduced ETA" result (OF)
2. You take the same control stream, and set all initial values to the final
parameter estimates of model (1) above, except you use the small value (may be
not 0.01 but 0.000001) as the initial value of the ETA that was fixed to zero
in model (1).
Model (2) is the not-reduced model, and it's OF should be less or equal to the
OF of model (1). If this is not the case, increase the number of significant
digits in the initial estimates of model (2) - take those from the final
estimates of model 1.
Without data, it is very difficult to offer more specific advice.
Also, what is the magnitude of the OF change? What is the estimate of the OMEGA
for the ETA in question?
Regards,
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: http://www.quantpharm.com
e-mail: LGibiansky at http://quantpharm.com
tel: (301) 767 5566<tel:%28301%29%20767%205566>
On 8/25/2013 8:42 AM, Xinting Wang wrote:
Dear Leonid,
I tried with your method and found the same result. The initial
estimation of the added ETA was set at 0.01, and the result showed an
increase of OFV. Please see below the $PK part of the control file for
more information. Many thanks.
Dear Bill,
Could you please explain that in a little bit more detail? I am pasting
the $PK part of the control file in case you could find the useful
information. Thanks a lot.
$PK
FA1=0
FA2=0
FA3=0
FA4=0
IF(DOSE.EQ.250) THEN
FA1=1
ENDIF
IF(DOSE.EQ.500) THEN
FA2=1
ENDIF
IF(DOSE.EQ.850) THEN
FA3=1
ENDIF
IF(DOSE.EQ.1000) THEN
FA4=1
ENDIF
F1=FA1+FA2*THETA(6)+FA3*THETA(7)+FA4*THETA(8)
TVCL=THETA(1)
TVV2=THETA(2)
TVKA=THETA(3)
TVQ=THETA(4)
TVV3=THETA(5)
CL=TVCL*EXP(ETA(1))
V2=TVV2*EXP(ETA(2))
KA=TVKA*EXP(ETA(5))
Q=TVQ*EXP(ETA(3))
V3=TVV3*EXP(ETA(4))
S2=V2/1000
S3=V3/1000
$ERROR
IPRE=F
IRES=DV-IPRE
W=F
IF(W.EQ.0) W = 1
IWRE = IRES/W
Y=F*(1+EPS(1))+EPS(2)
Best Regards
On 12 August 2013 20:50, Denney, William S.
<[email protected]<mailto:[email protected]>
<mailto:[email protected]<mailto:[email protected]>>> wrote:
Hi Xinting,
In a few rare cases, I've seen this happen if the model is
approaching nonconvergence. In those cases, typically the RSE on
one or more parameters will increase and the ratio of max to min
eigenvalues will increase substantially. Are you seeing either of
these?
Thanks,
Bill
On Aug 11, 2013, at 21:56, "Leonid Gibiansky"
<[email protected]<mailto:[email protected]>
<mailto:[email protected]<mailto:[email protected]>>> wrote:
Xinting,
Try to start from the initial conditions of your "reduced" model but
add that "reduced" ETA with the corresponding OMEGA equal to 0.01 or
other small number. If the control stream code is correct, the
objective function should decrease or retain the same value.
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: http://www.quantpharm.com
http://www.quantpharm.com
e-mail: LGibiansky at http://quantpharm.com
http://quantpharm.com
tel: (301) 767 5566<tel:%28301%29%20767%205566> <tel:%28301%29%20767%205566>
On 8/10/2013 10:23 PM, Xinting Wang wrote:
> Dear all,
>
> Does anyone witnessed such a phenomenon in NONMEM as when you
reduced an
> ETA, the OFV value, rather than increase, actually decreased?
It's quite
> against intuition, as individual estimation should be better than
> population estimation in that particular parameter. Both models,
whether
> having this ETA, converged very well.
>
> Best
>
> --
> Xinting
--
Xinting
--
Xinting