Skip to Main Content
Exposure-Response Modeling
book

Exposure-Response Modeling

by Jixian Wang
July 2015
Intermediate to advanced content levelIntermediate to advanced
351 pages
10h 2m
English
Chapman and Hall/CRC
Content preview from Exposure-Response Modeling
Sequential and simultaneous exposure–response modeling 95
to fit them, e.g., availa ble in SAS proc MODEL. However, since our goal is
to predict the c oncentration and to fit ER models, considering the following
marginal model derived from (4.62) is sufficient:
log(c
i1
) = θ
01
+ θ
1
log(d
i1
) + θ
12
log(d
i2
) + v
i1
log(c
i2
) = θ
02
+ θ
2
log(d
i2
) + θ
21
log(d
i1
) + v
i2
. (4.63)
They are almost the same as model (4.62), apart from the interaction term
θ
kk
c
ik
, c
ik
being replaced with d
ik
. The two models are equivalent in terms
of the dos e–exposure rela tionship. However, all parameters and v
ik
s in model
(4.63), not only θ
kk
, have different meanings from those in model (4.62). But
we do not introduce new names for simplicity. Also, even if the v
i1
and v
i2
are independent ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Computational Pharmacokinetics

Computational Pharmacokinetics

Anders Kallen
Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling

Zhangyang Wang, Yun Fu, Thomas S. Huang

Publisher Resources

ISBN: 9781466573215