Exposure-Response Modeling Methods and Practical Implementation-Chapman and Hal

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Jixian Wang-Exposure-Response Modeling_ Methods and Practical Implementation-Chapman and Hall_CRC (2015).pdf
The book emphasizes a number of important aspects: 1) causal inference
in exposure–response modeling, 2) sequential modeling in the view of measurement
error models, 3) dose–adjustment and treatment adaptation based
on dynamic exposure–response models, and 4) model-based decision analysis
linking exposure–response modeling to decision making. It tries to bridge
gaps between difference research areas to allow borrowing well-developed approaches
from each other. This is an application book, but it does not stop
at using simple models and approaches. It goes much further to recent developments
in a number of areas and describes implementation, methodologies
and interpretation of fitted models and statistical inference based on them.
Although the focus is on recent developments, no intensive knowledge on ER
modeling is needed to read the book and implement the methods. There are
models given in general forms with matrix notations. This may not be necessary
when a model is explicitly specified, but is useful to understand the
concept of some advanced approaches particularly when using software with
models specified in general forms such as R and SAS. The contents are arranged
to allow the reader to skip these formulae yet still be able to implement
the approaches.

A large number of practical and numerical examples can be found in this
book. Some illustrate how to solve practical problems with the approaches described,
while some others are designed to help with understanding concepts
and evaluating the performance of new methods. In particular, several examples
in clinical pharmacology are included. However, to apply approaches to a
real problem, it is crucially important to consult the literature and seek advice
from pharmacologists. A large number of SAS and R codes are included for the
reader to run and to explore in their own scenarios. Applied statisticians and
modelers can find details on how to implement new approaches. Researchers
and research students may find topics for, or applications of, their research.
It may also be used to illustrate how complex methodology and models can
be applied and implemented for very practical situations in relevant courses.

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