RE: Estimation method selection
Dear Tausif Ahmed,
Witty comments can be fun but here is a serious attempt to help you.
1. OFV values are not comparable between methods like FO, FOCE and
LAPLACE. You can apply an Monte Carlo Importance Sampling step after each of
these estimation methods to generate comparable OFV values (see example
below and read more in
ftp://nonmem.iconplc.com/Public/nonmem712/intro712.pdf). However I am not
sure that I think that the estimation method needs to be chosen in that way.
For your average continuous data problem FOCE with interaction should be a
sufficient method. FO are well known to be biased and should only be used if
practical problems such as runtimes etc. prohibits you from using other
methods.
; After the SAEM method, obtain good estimates of the marginal density
(objective function), along with good estimates of the standard errors.
$EST METHOD=IMP INTERACTION EONLY=1 NITER=5 ISAMPLE=3000 PRINT=1 SIGL=8
SEED=123334 CTYPE=3 CITER=10 CALPHA=0.05
2. OFV values are comparable across different hierarchical structure
models. There are a two parameter (i.e. approximately a 2 degree of freedom)
difference between a 1 compartment and a 2 compartment model. However, look
out because there are several possible caveats in these comparisons
especially with regards to censored observations [I,II,III].
I. Byon W, Fletcher CV, Brundage RC. Impact of censoring data
below an arbitrary quantification limit on structural model
misspecification. J Pharmacokinet Pharmacodyn. 2008 Feb;35(1):101-16.
II. Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based
approaches to handling data below the quantification limit using NONMEM VI.
J Pharmacokinet Pharmacodyn. 2008 Aug;35(4):401-21.
III. Bergstrand M, Karlsson MO. Handling data below the limit of
quantification in mixed effect models. AAPS J. 2009 Jun;11(2):371-80.
Kind regards,
Martin Bergstrand, PhD
Pharmacometrics Research Group
Dept of Pharmaceutical Biosciences
Uppsala University
Sweden
[email protected]
Visiting scientist:
Mahidol-Oxford Tropical Medicine Research Unit,
Bangkok, Thailand
Phone: +66 8 9796 7611
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of tausif Ahmed
Sent: Sunday, October 09, 2011 7:02 PM
To: [email protected]
Subject: [NMusers] Estimation method selection
Dear nmusers
I am new to the field of pop pk. I am developing a Pop PK model of a
compound by combining sparse PK data from 3 different mouse PK studies. I
have following doubts/queries:
1. When we tried diff. estimation methods in nm 7.1 such as FO, FOCE or
laplace. We got lowest OFV with laplace. Can we select the estimation method
based on only OFV. We got the estimates in both FO and laplace but
variability was reaching the boundary in case of FO.
-Is it correct to make decision based on OFV across different models (one
comp vs two comp). The diagnostic plots seem to be similar however
variability was less with laplacian method as compared to FO.
Which method of estimation would be most appropriate.
- Is it correct to compare OFV across difeerent estimation methods (viz FO;
FOCE; Laplace)
- Is it correct to compare OFV across difeerent PK models (viz 1 comp; 2
comp etc)
Inputs from experts are welcomed.
Tausif Ahmed PhD
Piramal Life Sciences Ltd, India