Pharm Stat. 2015 Jan-Feb;14(1):63-73. doi: 10.1002/pst.1660. Epub 2014 Nov 18.

Implementation of maximin efficient designs in dose-finding studies.

Pharmaceutical statistics

Ellinor Fackle-Fornius, Frank Miller, Hans Nyquist

PMID: 25405333 DOI: 10.1002/pst.1660

Abstract

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four-step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose-finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design.

© 2014 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

Keywords: H-algorithm; clinical study; dose-response model; extension of parameter set; maximin efficient design; optimal design

MeSH terms

• Dose-Response Relationship, Drug*
• Humans
• Models, Theoretical*
• Research Design / standards*
• Research Design / statistics & numerical data

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't