Designing dose finding studies with an active control for exponential families

TL;DR

This paper extends the design of dose finding studies with an active control to models based on exponential families, especially for count data, providing new optimal design strategies and comparing different distributional assumptions.

Contribution

It broadens the scope of optimal design methods for dose finding with active controls to include exponential family models, including discrete data, and explores their practical applications.

Findings

Optimal designs can be adapted for exponential family models.

Design efficiency varies with distributional assumptions.

New designs are illustrated with real-world examples.

Abstract

In a recent paper Dette et al. (2014) introduced optimal design problems for dose fnding studies with an active control. These authors concentrated on regression models with normal distributed errors (with known variance) and the problem of determining optimal designs for estimating the smallest dose, which achieves the same treatment effect as the active control. This paper discusses the problem of designing active-controlled dose fnding studies from a broader perspective. In particular, we consider a general class of optimality criteria and models arising from an exponential family, which are frequently used analyzing count data. We investigate under which circumstances optimal designs for dose fnding studies including a placebo can be used to obtain optimal designs for studies with an active control. Optimal designs are constructed for several situations and the differences arising from different distributional assumptions are investigated in detail. In particular, our results are applicable for constructing optimal experimental designs to analyze active-controlled dose fnding studies with discrete data, and we illustrate the efficiency of the new optimal designs with two recent examples from our consulting projects.