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
Quoted reply history
________________________________
From: [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®: 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
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