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RE: Implementing a Kalman Filter based optimization in NONMEM

From: E.Olofsen
Date: Mon, 14 Sep 2015 08:39:50 +0000

Dear John,

In the code by Tornoe et al., state variables A(i) are stored in the Ai var=
iables, and retrieved by statements Ai = Ai. Such recursive code is descr=
ibed in NONMEM's help on abbreviated code. Although the A(i) are associated=
 with differential equations, you could perhaps still use such recursive st=
atements, indicating that you want to store and retrieve information?

Best regards,

From: owner-nmusers
 of John Warner [John.Warner
Sent: Sunday, September 13, 2015 11:24 PM
To: nonmem usersgroup
Subject: [NMusers] Implementing a Kalman Filter based optimization in NONME=

Dear NONMEM users
I am attempting to implement a Kalman Filter based optimization in NONMEM u=
sing $PRED directly. The method I am attempting to implement is similar i=
n spirit to that presented in Tornoe et. al. (2005) (and the NONMEM 7.3 man=
ual) except that I have no need for a differential equations solver. In e=
ffect 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 fin=
d 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 c=
all for the same subject. I assume that something like this must be done b=
y ADVAN6 when differential equations are solved.

I would be very grateful for any advice on this.


Tornoe et. al. Stochastic Differential Equations in NONMEM®: Implementa=
tion, Application, and Comparison with Ordinary Differential Equations Ph=
armaceutical 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

Received on Mon Sep 14 2015 - 04:39:50 EDT

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