Optimal Relaxed Designs of Experiments, with Pharmaceutical Applications
Abstract
This thesis was motivated by the collaborative research undertaken by QMUL and Pfizer UK
into improving experiments at pre-clinical drug development. In theory, the most efficient
designs for these particular experiments, as well as for many other studies, are optimal
designs. Since, however, their implementation poses challenges — and several emerged
during the project — optimal designs are uncommon in practice.
To address these challenges the thesis introduces a comprehensive design framework,
which both generalizes and simplifies optimal design. At the core of this framework are
optimal relaxed designs, seldom considered before. Like a standard design measure a relaxed
design has non-integer replications and is mathematically tractable; unlike the former,
whose replications must sum to one, it allows the replications total to be unconstrained.
The methodology discussed in this thesis assumes design of experiments for parameter
estimation, given a response model, but also applies to broader problems. Although the
motivation and applications come from the pharmaceutical project, the ultimate goal is to
develop an intuitive and versatile design toolkit for experimenters in various practical fields.
Authors
Volkov, OlegCollections
- Theses [3822]