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Dear all,
I am trying to build a population PK model with 2 compartment, first order absorption. The raw data
has four doses, and I included a fractional absorption compared with the lowest dose in the control
file. The base model has already had 13 THETA values. So, I am wondering, if after base model and I
get an estimation of the Fractional Absorptions, could I set them (Estimation of Fractional
Absorption) to fixed values, so that adding more covariate might work out faster and avoid the error
message in the covariance step? Many thanks.
Below is the PK part of the control value.
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(10)+FA3*THETA(11)+FA4*THETA(12)
BWNORM=BW/66
TVCL=THETA(1)
CL=TVCL*BWNORM**THETA(6)*EXP(ETA(1))
TVV2=THETA(2)
V2=TVV2*BWNORM**THETA(7)*EXP(ETA(2))
TVKA=THETA(3)
KA=TVKA*BWNORM**THETA(13)
TVQ=THETA(4)
Q=TVQ*BWNORM**THETA(8)*EXP(ETA(3))
TVV3=THETA(5)
V3=TVV3*BWNORM**THETA(9)*EXP(ETA(4))
S2=V2/1000
S3=V3/1000
--
Xinting
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Xinting,
This kind of question is probably better sent to nmusers rather than pharmpk. The moderator of
pharmpk censors previous postings which makes it hard to interpret responses because the background
information is not included.
In this particular case you can reduce the number of THETAS by 4 by not attempting something with
little expectation of value by trying to estimate allometric exponents for CL, V2, Q and V3. The
allometric exponent is known so why waste your time trying to estimate it? If you have a small data
set with a narrow distribution of weights e.g. 100 subjects with a CV of weight of 20% then you
cannot expect to get a precise estimate of the exponent (see Anderson & Holford 2008).
It makes more sense to standardize parameters to 70 kg rather than an arbitrary value of 66 kg. See
Holford, Yeo, Anderson 2013).
You should NOT fix the estimates of F at different doses when investigating other covariates. If
other covariates are explanatory then clearly your base model estimates indicate some model
misspecification and so your estimate of F will be wrong. If there are really differences in F with
dose then these should be estimated as part of your final model.
It is a common mistake to assume that failure of convergence in NONMEM is an indication of a bad
fit. This is not true. Many people have looked at this and found no evidence for an association
between convergence and goodness of fit. Go and read the nmusers archives
(http://www.mail-archive.com/nmusers.-a-.globomaxnm.com/msg01087.html). You should evaluate your model
based on parameter plausibility and VPCs (don't waste your time with residual plots and so called
diagnostic plots of PRED vs DV, IPRED vs DV).
Best wishes,
Nick
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Dear Nick,
Thanks very much for your kind reply.
I understand that in general the rule of allometric scaling for CL follows
the 3/4 rule. As you and B.J. Anderson pointed out in your paper (B.J.
Anderson, N.H.G. Holford, 2008, Annu. Rev. Pharmacol. Toxicol, 48:303-32),
no evidence has been provided to reject this rule. However, as you also
mentioned in this paper, "experimental designs for estimating clearance are
much less robust and the error in estimation of the allometric coefficient
can be expected to be larger...".
Theoretically, we do not have proof to reject this, but in the particular
case, using 3/4 might actually increase OFV and thus reduce the
goodness-of-fit. When I replaced the 0.75 with a THETA on CL, the OFV value
decreased by 15, which I think should be a significant impact on
estimation. However, replace them all with THETAs could on the other hand
decrease the ability of successful estimation. How should balance this
problem?
Regards
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Xinting,
You wrote:
"Theoretically, we do not have proof to reject this, but in the particular case, using 3/4 might
actually increase OFV and thus reduce the goodness-of-fit. When I replaced the 0.75 with a THETA on
CL, the OFV value decreased by 15, which I think should be a significant impact on estimation.
However, replace them all with THETAs could on the other hand decrease the ability of successful
estimation. How should balance this problem? "
As I pointed out in my previous email ("Go and read the nmusers archives
(http://www.mail-archive.com/nmusers.aaa.globomaxnm.com/msg01087.html))" NONMEM models should not be
judged by "successful termination". Did you read and understand that?
In general fixing parameters for all those parameters which theory provides a value will stabilise
your estimation.
Theory is always preferable to empirical results. So in fact there is no problem.
Best wishes,
Nick
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Xinting
I think something of more general interest is considering whether you wish to use "a priori"
covariate models or "a posteriori" covariate models.
A priori covariate models are derived based on previous theoretical work or empirical work by others
and have been tried in other circumstances (e.g. allometric scaling, Cockroft and Gault for eGFR
etc).
A posteriori covariate models are derived based on your data only to provide the best fit for your
current data set.
This is a conceptual choice and defines the way you build models. There is no right and wrong about
what to do it - it's just up to you.
Note, however, that a priori covariate models have generally stood the test of time and shown to be
useful in a wide spectrum of the population (e.g. eGFR models) whereas an posteriori model has only
been tested in your specific population.
The question, from an epidemiology perspective is: are you looking for internal or external
validity. The question from a pharmacometrics perspective is are you interested in describing your
data or predicting into new settings.
On the whole I feel more comfortable with using tried and tested models ...
Regards
Steve
--
Professor Stephen Duffull
Chair of Clinical Pharmacy
School of Pharmacy
University of Otago
PO Box 56 Dunedin
New Zealand
E: stephen.duffull.-a-.otago.ac.nz
www.pharmacometrics.co.nz
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