Optimal Designs in Multiple Group Random Coefficient Regression Models

TL;DR

This paper investigates optimal experimental designs for multiple group random coefficients regression models, focusing on treatments and control groups, with applications to cluster randomized trials, and provides numerical illustrations.

Contribution

It introduces optimal design strategies for estimating fixed and random effects in multi-group models, enhancing efficiency in cluster randomized trial analyses.

Findings

Derived A-, D-, and E-optimal designs for treatment effect estimation.

Numerical examples demonstrate the effectiveness of the proposed designs.

Guidelines for practical implementation in clinical and experimental studies.

Abstract

The subject of this work is multiple group random coefficients regression models with several treatments and one control group. Such models are often used for studies with cluster randomized trials. We investigate A-, D- and E-optimal designs for estimation and prediction of fixed and random treatment effects, respectively, and illustrate the obtained results by numerical examples.