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]>