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Error models for log-transformed data

From: Han, Kelong <keh45>
Date: Mon, 27 Apr 2009 18:16:24 -0400

Dear NMusers,

I am working on a PK model using log-transformed data. I have read previous=
 discussions on NMusers regarding this, and they are really helpful, but I =
am still a little bit confused about the following questions. I would great=
ly appreciate it if someone could make it clear:

1. Dr. Mats Karlsson suggested Y=LOG(F)+SQRT(THETA(x)**2+THETA(y)**2/F**2=
)*ERR(1) with $SIGMA 1 FIX as an equivalent error structure to the
additive+proportional error model on the normal scale. What is the rational=
e of fixing $SIGMA 1?

2. Dr. Stu Beal and Dr. William Bachman suggested the "double exponential e=
rror model": Y = LOG(F+M) + (F/(F+M))*ERR(1) + (M/(F+M))*ERR(2) without f=
ixing $SIGMA. The Goodness-of-Fit plot looks slightly better using this err=
or model in my study. What an error structure on the normal scale is this "=
double exponential error model" equivalent to?

3. Compared to the simplest error model Y=LOG(F)+ERR(1), the two error mo=
dels mentioned above contain additional THETA's. Are these additional THETA=
's accounted for in the calculation of the objective function value? This e=
specially bothers me because the "double exponential error model" leads to =
a lower OFV compared to Y=LOG(F)+ERR(1) (also slightly better Goodness-of=
-Fit plot) in my study.

Sorry for the length. Would anyone please give me some explanations or refe=

Thanks a lot!

Kelong Han

PhD Candidate=
Received on Mon Apr 27 2009 - 18:16:24 EDT

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