As a follow up to the last tip and some recent discussion
on nmusers, this tip will address "what do I do with DV=0
records in data sets when log-transforming my data?"
In the previous tip (#9), the data came from an IV study (actually
the phenobarbital data that is distributed with NONMEM was
ln-transformed) with no zero observations (fortuitously and
by design). In that situation, the following code can be used
to provide individual predictions (and avoid the ln(DV)=log(0)
which would occur for the dosing records):
However, in the situation where one has data with observations
with DV=0, you have to decide how you are going to treat those
records. As an example, I have ln-transformed the theopp data
that is distributed with NONMEM. In preparing the transformed
data set, a column was added with ln(DV). For ln(0) records,
0 was entered as the ln(DV). Now, how do you handle the model
code?
Suggestions were proposed to set the ln(0) equal to some
"small value":
$ERROR CALLFL=0 IPRED
= -3 ;similar to above code but ipred=-3 instead of
ipred=0 IF(F.GT.0)
IPRED = LOG(F) Y
= IPRED + EPS(1)
Another suggestion is to simply delete the observations
records with DV=0 if they occur right after the dose and
there are not "a lot" of them. An argument can be made that
these are BQL's and could be deleted.
Use the "double-exponential" residual error model:
Y=LOG((F)+THETA(n))+(F*ERR(1))/(F+THETA(n))+(THETA(n)*ERR(2))/(F+THETA(n))
;code does not require log(0) protection except if IPRED
wanted
IPRED=0
IF(F.GT.0) IPRED=LOG(F)
For the log transformed theophylline data, the "best" fit to
the data was obtained using option 2. and 3. Both gave an equivalent
fit and parameter estimates. Options 1.A. and 1.B, gave fits
with significantly higher objective function values and bias
for this data. The arbitrary selection of the "small" value
had a significant effect on the fit. With option 3., the value
of m, the "positively constrained variance parameter" (THETA(n),
here), was about 0.2, presumably near the LOQ. The difference
in objective function between 2. and 3. was ~6.4 (lower for
3. but with two more parameters in the model). This again suggests,
why not remove the DV=0 observation records? What are YOUR THOUGHTS?
[errors may be reported to the author and are welcomed.]
[Generic Disclaimer: Verify that this code or any other code
you receive from an outside source works with YOUR DATA.]
PRODUCTS:
NONMEM® USER TIPS
CALLFL=0