Dear nmusers,

I'm working on a pharmacokinetic population analysis of a data set in
NONMEM for my master thesis. The data set I'm analysing consists of 12
hours concentration-time profiles. A 2-compartment model with Erlang
distribution describes the data well, and I'm now validating my model.
However, I've run into some questions.

I've performed a data splitting analysis, and have some questions about
predictive performance/simulation in NONMEM.

1. What is the difference between using different seeds and subproblems
when doing a simulation? And how many seeds/subproblems are generally
considered to be needed?

2. Simulating my subsets for the data splitting seems to give a
over-prediction of the concentrations observed. Is there any way I can put
in the C0/C2 concentration, in order to give NONMEM more information and
hopefully better PRED when doing a simulation? Only age was found to be a
significant covariate, and I suspect the reason for over-predicting the
concentrations is that NONMEM needs more information.

3. When having different time measurements, how do I calculate out me (mean
error) and rmse (root mean square error) for the subset; can I use the
average for the different time measurements in the subset?

Part of the inputfil:
ID AMT TIME DV MDV SS II CMT AGE RATE
8  225  0       0   1   2   12  1  59  0
8  0     0       0   0   0   0    8  59  0
8  0     0.23   0   0   0   0    8  59  0
8  0     0.48   0   0   0   0    8  59  0
8  0     0.98   0   0   0   0    8  59  0
8  0     1.43   0   0   0   0    8  59  0
8  0     1.98   0   0   0   0    8  59  0
8  0     3.03   0   0   0   0    8  59  0
8  0     4.07   0   0   0   0    8  59  0
8  0     5.98   0   0   0   0    8  59  0
8  0     8.03   0   0   0   0    8  59  0
8  0     9.97   0   0   0   0    8  59  0
8  0     1.90   0   0   0   0    8  59  0

Part of the controlfile:
$PROB
$DATA
$INPUT
$SUBROUTINE  ADVAN5 SS5;
$MODEL
$PK
$ERROR

$THETA
  (FIX 7.98) ; K12 (B)
  (FIX 27.2) ; Q/F
  (FIX 57) ; V8
  (FIX 203) ; V9
  (FIX 23.4) ; CL/F
  (FIX 0.107) ; age effect

$OMEGA
(FIX 0.06)
(FIX 0.09)
(FIX 0.27)
(FIX 0.91)
(FIX 0.02)

$SIGMA
(FIX 0.02)
(FIX 1370)

$SIMULATION (49682607) ONLYSIMULATION SUBPROBLEMS=10

$TABLE


Your help is highly appreciated, thank you in advance.


Best regards,
Live

Live Storehagen
Master student
Institute of Pharmacy
University of Oslo
Norway


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