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
100 Exposure-Response Modeling: Methods and Practical Implementation
and note that the assumption leads to a simple solution to the problem, but
may not be reasonable for some practical scenarios. These models are exactly
the same as models (4.66) if g(c
i
, β) is linear, although the notation is dif-
ferent, with c
i
and d
i
replaced with c
i
and c
i
, respectively. Therefore, the
same RC approaches for models (4.66), such as the weighted LS appr oaches
described above, apply here. For example, for a linear y-part model, the RC
approach first fits model c
i
= h(c
i
, θ) + e
i
and estimate E(c
|c
i
) by h(c
i
,
ˆ
θ).
Then a weighted LS can be used to estimate β.
4.7.3 Using a surrogate for C
max
: An example
Here we c onsider a practical example of using a surroga te for ...
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