Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R
describes the common underlying theory of multiple comparison procedures through …

A good understanding and characterization of the dose response relationship of any new
compound is an important and ubiquitous problem in many areas of scientific investigation …

Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical
industry, academia, and regulatory agencies, this volume explores the rapidly growing area …

Maintaining a practical perspective, Bioequivalence and Statistics in Clinical Pharmacology,
Second Edition explores statistics used in day-to-day clinical pharmacology work. The book …

This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-
at-a-time Bayesian sequential design problem in the presence of model uncertainty where …

Deriving optimal designs for nonlinear models is, in general, challenging. One crucial step is
to determine the number of support points needed. Current tools handle this on a case-by …

A key objective in the clinical development of a medicinal drug is the determination of an
adequate dose level and, more broadly, the characterization of its dose response …

In this article the MCPMod package for the R programming environment will be introduced. It
implements a recently developed methodology for the design and analysis of dose …

This article reports the results of a meta-analysis based on dose–response studies
conducted by a large pharmaceutical company between 1998–2009. Data collection …