Optimal approximate designs for estimating treatment contrasts resistant to nuisance effects

TL;DR

This paper develops methods for designing experiments that efficiently estimate treatment effects while being resistant to nuisance factors like time trends or blocking, using optimal approximate designs and linear programming.

Contribution

It introduces a framework for constructing optimal approximate designs that are resistant to nuisance effects, with explicit solutions for common contrast and optimality criteria.

Findings

Optimal treatment proportions derived from marginal models

Explicit forms of optimal designs for specific contrasts

Method to construct efficient exact designs via linear programming

Abstract

Suppose that we intend to perform an experiment consisting of a set of independent trials. The mean value of the response of each trial is assumed to be equal to the sum of the effect of the treatment selected for the trial, and some nuisance effects, e.g., the effect of a time trend, or blocking. In this model, we examine optimal approximate designs for the estimation of a system of treatment contrasts, with respect to a wide range of optimality criteria. We show that it is necessary for any optimal design to attain the optimal treatment proportions, which may be obtained from the marginal model that excludes the nuisance effects. Moreover, we prove that for a design to be optimal, it is sufficient that it attains the optimal treatment proportions and satisfies conditions of resistance to nuisance effects. For selected natural choices of treatment contrasts and optimality criteria, we calculate the optimal treatment proportions and give an explicit form of optimal designs. In particular, we obtain optimal treatment proportions for comparison of a set of new treatments with a set of controls. The results allow us to construct a method of calculating optimal approximate designs with a small support by means of linear programming. As a consequence, we can construct efficient exact designs by a simple heuristic.