Kelong,
If you are really and truly only interested in a single subject then the
only source of random variability will be the residual error (ERR). I
would therefore use the final estimate of OMEGA you obtained from
fitting the single subject in order to compute the prediction interval.
I should say that what you are doing is very unusual. In the real world
people are usually interested in more than one subject and so there are
random effects for the PK parameters (ETA) as well as for the residual
error (ERR). Are you really and truly sure that you are only interested
in one single subject all by himself?
Note that even within one subject there is typically dose to dose
variation in the parameters (especially KA and F1) which you can model
by adding an ETA random effect for each different dose. See Karlsson MO,
Sheiner LB. The importance of modeling interoccasion variability in
population pharmacokinetic analyses. Journal of Pharmacokinetics &
Biopharmaceutics. 1993; 21(6):735-50.
Nick
Han, Kelong wrote:
Dear NONMEM users,
I am trying to calculate and plot the 95% prediction interval (PI) for a
single-subject multiple-dosing PO dataset by simulating 1000 DV values.
It seems that bigger initial estimate of omega ($OMEGA) leads to wider 95%
prediction band. I understand that OMEGA directs the variability in
"ERR(1)" in single-subject data, but I am still confused.
Could anyone help me pick up a $OMEGA to calculate 95% PI, or solve this
problem in another way? Thanks!
Below is the control stream (the best-fit THETA values were used as
initials):
--------------------------------------------------
$DATA po.csv IGNORE=C
$INPUT ID TIME CONC=DV AMT MDV CMT
$SUBROUTINE ADVAN2 TRANS2
$PK
CL = THETA(1)
V = THETA(2)
KA = THETA(3)
S2 = V
F1 = 1
$ERROR
IPRED=F
Y=F+ERR(1)
$THETA (0.398)
$THETA (64.3)
$THETA (0.425)
$OMEGA 1.2
$SIMULATION (324422) SUBPROBLEMS=1000
$ESTIMATION METHOD=0 NOABORT MAXEVAL=9999 PRINT=0
$COVARIANCE
$TABLE TIME DV IPRED NOPRINT NOHEADER FILE=
---------------------------------------------------------
Any input would be greatly appreciated.
Thanks!
Sincerely
--
Kelong Han
PhD student
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090
www.health.auckland.ac.nz/pharmacology/staff/nholford
