RE: Implementing a Kalman Filter based optimization in NONMEM
Thanks Eric
Yours seems like a very straight-forward and complete solution. However, if I
am not mistaken, the Ai variables are created by $model and I am not sure that
$model can be used by a user supplied $pred. If it can, there remains the
problem of calling the model subroutine. I assume that this would be done with
verbatim code but I am not sure where to put such a call in my control stream.
The documentation (html help files) states that $model is called only once by
the PREDPP subroutines that use it. Perhaps I could put such a call in the
$input or $pk records? $PK would seem more logical but it is not clear that
this record is available outside of predPP.
I am very interested to hear any additional responses to this.
Thanks again.
John
John H. Warner, PhD, MBA
Director, Biostatistics
CHDI Management / CHDI Foundation
155 Village Boulevard, Suite 200
Princeton, NJ, 08540
(609) 945-9644: office
(609) 751-7345: cell
(609) 452-2160: fax
[email protected]<mailto:[email protected]>
Quoted reply history
From: [email protected] [mailto:[email protected]]
Sent: Monday, September 14, 2015 4:40 AM
To: John Warner; [email protected]
Subject: RE: Implementing a Kalman Filter based optimization in NONMEM
Dear John,
In the code by Tornoe et al., state variables A(i) are stored in the Ai
variables, and retrieved by statements Ai = Ai. Such recursive code is
described in NONMEM's help on abbreviated code. Although the A(i) are
associated with differential equations, you could perhaps still use such
recursive statements, indicating that you want to store and retrieve
information?
Best regards,
Erik
________________________________
From: [email protected]<mailto:[email protected]>
[[email protected]] on behalf of John Warner
[[email protected]]
Sent: Sunday, September 13, 2015 11:24 PM
To: nonmem usersgroup
Subject: [NMusers] Implementing a Kalman Filter based optimization in NONMEM
Dear NONMEM users
I am attempting to implement a Kalman Filter based optimization in NONMEM using
$PRED directly. The method I am attempting to implement is similar in spirit
to that presented in Tornoe et. al. (2005) (and the NONMEM 7.3 manual) except
that I have no need for a differential equations solver. In effect I can solve
the differential equations analytically but I still need to estimate a random
walk error term. Adapting the procedure of Tornoe et. al. 2005 seems
straight-forward except that, it seems to me, I need to find a way to store the
state vector and associated partial derivatives at the end of a call to $PRED
and to retrieve them at the beginning of the next call for the same subject. I
assume that something like this must be done by ADVAN6 when differential
equations are solved.
I would be very grateful for any advice on this.
Best
John
Tornoe et. al. Stochastic Differential Equations in NONMEM(r):
Implementation, Application, and Comparison with Ordinary Differential
Equations Pharmaceutical Research, Vol. 22, No. 8, August 2005 2005)
John H. Warner, PhD, MBA
Director, Biostatistics
CHDI Management / CHDI Foundation
155 Village Boulevard, Suite 200
Princeton, NJ, 08540
(609) 945-9644: office
(609) 751-7345: cell
(609) 452-2160: fax
[email protected]<mailto:[email protected]>