Exposure-Response Modeling: Methods and Practical Implementation, 1st Edition (Hardback) book cover

Exposure-Response Modeling

Methods and Practical Implementation, 1st Edition

By Jixian Wang

Chapman and Hall/CRC

351 pages | 27 B/W Illus.

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pub: 2015-07-17
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Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships

Written by an applied statistician with extensive practical experience in drug development, Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling, from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods.

The book describes using measurement error models to treat sequential modeling, fitting models with exposure and response driven by complex dynamics, and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis, causal inference to eliminate confounding biases, and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation.

Many examples illustrate the use of exposure-response modeling in experimental toxicology, clinical pharmacology, epidemiology, and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios.

Although application oriented, this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition, students can see how complicated methodology and models are applied to practical situations.


"…the book is worth reading as it takes the reader all the way from basic to state-of-the-art exposure-response modeling approaches and challenges. It focuses on detailed mathematical derivations, with many insights based on practical experience. Moreover, many data examples are accompanied by software code …"

~Biometrical Journal

" . . . the greatest strength of this book is that the models and methodologies are always motivated and explained by applications and examples, which effectively communicates to readers the basic ideas behind complex methodologies. Also, practical implementation and computer code are discussed alongside the methods, which will help readers to apply the methods to their own data."

~University of Texas Health Science Center at Houston

Table of Contents


Multifaceted exposure-response relationships

Practical scenarios in ER modeling

Models and modeling in exposure-response analysis

Model-based decision-making and drug development

Drug regulatory guidance for analysis of exposure-response relationship

Examples and modeling software

Basic exposure and exposure-response models

Models based on pharmacological mechanisms

Statistical models


Semiparametric and nonparametric models

Comments and bibliographic notes

Dose-exposure and exposure-response models for longitudinal data

Linear mixed models for exposure-response relationships

Modeling exposures with linear mixed models

Nonlinear mixed ER models

Modeling exposure with a population PK model

Mixed effect models specified by differential equations

Generalized linear mixed model and generalized estimating equation

Generalized nonlinear mixed models

Testing variance components in mixed models

Nonparametric and semiparametric models with random effects

On distributions of random effects

Bibliographic notes

Sequential and simultaneous exposure-response modeling

Joint models for exposure and response

Simultaneous modeling of exposure and response models

Sequential exposure-response modeling

Measurement error models and regression calibration

Instrumental variable methods

Modeling multiple exposure and response

Internal validation data and partially observed and surrogate exposure measures

Comments and bibliographic notes

Exposure-risk modeling for time-to-event data

An example

Basic concepts and models for time-to-event data

Dynamic exposure model as a time varying covariate

Multiple TTE and competing risks

Models for recurrent events

Frailty: Random effects in TTE models

Joint modeling of exposure and time to event

Interval censored data

Model identification and misspecification

Random sample simulation from exposure-risk models

Comments and bibliographic notes

Modeling dynamic exposure-response relationships

Effect compartment models

Indirect response models

Disease process models

Fitting dynamic models for longitudinal data

Semiparametric and nonparametric approaches

Dynamic linear and generalized linear models

Testing hysteresis

Comments and bibliographic notes

Bayesian modeling and model-based decision analysis

Bayesian modeling

Bayesian decision analysis

Decisions under uncertainty and with multiple objectives

Evidence synthesis and mixed treatment comparison

Comments and bibliographic notes

Confounding bias and causal inference in exposure-response modeling


Confounding factors and confounding biases

Causal effect and counterfactuals

Classical adjustment methods

Directional acyclic graphs

Bias assessment

Instrumental variable

Joint modeling of exposure and response

Study designs robust to confounding bias or allowing the use of instrument variables

Doubly robust estimates

Comments and bibliographic notes

Dose-response relationship, dose determination, and adjustment

Marginal dose-response relationships

Dose-response relationship as a combination of dose-exposure and exposure-response relationships

Dose determination: Dose-response or dose-exposure-response modeling approaches?

Dose adjustment

Dose adjustment and causal effect estimation

Sequential decision analysis

Dose determination: Design issues

Comments and bibliographic notes

Implementation using software

Two key elements: Model and data

Linear mixed and generalized linear mixed models

Nonlinear mixed models

A very quick guide to NONMEM




About the Author

Jixian Wang is a principal statistician at Celgene International, Switzerland. He worked on drug development for 14 years at GSK and Novartis Pharma and was an academic researcher at Edinburgh University and Dundee University, where he is still an honorary research fellow. His research interests include statistical methodology and its applications to real problems in pharmaceuticals, including exposure-safety, PKPD modeling, treatment/dose selection, health economics, benefit-risk and health technology assessments, and optimal trial design.

About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Pharmacology
MEDICAL / Pharmacy