Huali,
A quick note on item number 2. If the model is predicting F=0, the
selection of IPRED=-3 could be altering the fit of the model.
Try the following:
$ERROR
CALLFL=0
FLAG=0
IF(AMT.NE.0) FLAG=1 ;set flag=1 for dose records
;prevents log of 0 for dose records only
;changing IPRED (or F) for dose records does not change the computation
;of the objective function value.
IPRED=LOG(F+FLAG)
W=1 ;additive error model
IRES=DV-IPRED
IWRES=RES/W
Y=IPRED +EPS(1)
Changing IPRED (or F) on concentration records alters the computation of
the objective function value. This should only be used as a last resort.
If you actually predict a zero for a concentration record, I suggest
evaluating the data first. Does the data make sense or is there an error
in sample collection time or dose times (especially check for a missing
dose or an incorrect ADDL value)?
If everything is good with the data, then you may not have any other
option than to alter the predicted concentration. If this is the case,
then I suggest testing different values of IPRED using your code. Run
the model using IPRED=-3 then IPRED=-4 then IPRED=-5, etc. until two
runs have the same MVOF (Since the log(0)=-infinity, IPRED=-3 may not be
small enough). I would then use the smallest IPRED that you tested to
minimize the impact of changing a predicted concentration on your
modeling results.
Regards,
Luann Phillips
Director PK/PD
Cognigen Corporation
Huali Wu wrote:
Dear NMusers:
I have two questions regarding model fitting.
1. FOCE vs. FOCE with INTERACTION. I have a rich data from phase I
study. Drug was administered by iv infusion. I used a one-compartment
model with nonlinear clearance (Michaelis-Menten kinetics) to fit this
data. And I tried both FOCE and FOCE with INTERACTION. The FOCE method
generated a reasonable fit, while FOCE with INTERACTION generated a
biased prediction (underpredict) of concentration. I thought FOCE
with INTERACTION usually generate better result than FOCE. Does this
mean my model is just not good enough? I used a proportional plus
additional residual error model.
2. I also tried to fit log transformed data, but in the PRED vs. DV
plot, the points at lower concentrations are much more scattered than
those at higher concentrations. And this forms a trend that points are
getting closer and closer to the line as the concentration goes up. Does
that mean log transformation of my data is not appropriate or something
is wrong with my residual error model? The concentration ranges from 2
ng/ml to 1600 ng/ml. The residual error model I used is listed as below:
$ERROR
CALLFL=0
IPRED=-3
IF(F.GT.0)IPRED=LOG(F); to avoid LOG(0)run-time error
Y=IPRED+EPS(1)
Any suggestion will be highly appreciated!
Huali
