arXiv subjects

Damianos Michaelides

Publications and source records attributed to Damianos Michaelides.

4 recordsLinked to original sources

arXiv2026

A framework for classifying and visualising experimental designs when subjects are measured repeatedly

When running experiments that involve humans or animals, for example in clinical or pre-clinical research, it is often the case that multiple measurements are taken on the experimental subjects. This may involve measuring subjects repeatedly over time to track any trends, administering a sequence of treatments to compare their effects within-subject, or taking multiple technical replicate samples of each subject to obtain a more reliable average response. However, it appears that there is some confusion as to what constitutes a 'repeated measure' and what does not. This paper clarifies this confusion by defining characteristics a design may possess when the subjects are assessed multiple times. These characteristics are delineated based on whether the subjects are the experimental units, if the experimental units are measured repeatedly or not, and which randomisation strategy is used. Experimental designs can then be classified using these characteristics and visualised using Hasse diagrams.

stat.AP↗

arXiv2025

hassediagrams:an R package that generates the Hasse diagram of the layout structure and the restricted layout structure

With the advent of modern statistical software, complex experimental designs are now routinely employed in many areas of research. Failing to correctly identify the structure of the experimental design can lead to incorrect model selection and misleading inferences. This paper describes the hassediagrams package in R that determines the structure of the design, summarised by the layout structure, and generates a Hasse diagram of the layout structure. By considering the randomisation performed, in conjunction with the layout structure, a set of randomisation objects can be defined that form the restricted layout structure. This structure can also be visualised using a generalisation of the Hasse diagram. Objects in the restricted layout structure can be used to identify the terms to include in the statistical model. The use of the procedure thus ensures consistency of model selection due to the systematic approach taken to generate the model.

stat.CO↗

arXiv2025

Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study

A Bayesian optimal experimental design framework is developed for experiments where settings of one or more variables, referred to as profile variables, can be functions. For this type of experiment, a design consists of combinations of functions for each run of the experiment. Within a scalar-on-function linear model, profile variables are represented through basis expansions. This allows finite-dimensional representation of the profile variables and optimal designs to be found. The approach enables control over the complexity of the profile variables and model. The method is illustrated on a real application involving dynamic feeding strategies in an Ambr250 modular bioreactor system.

stat.ME↗

arXiv2025

fdesigns: Bayesian Optimal Designs of Experiments for Functional Models in R

This paper describes the R package fdesigns that implements a methodology for identifying Bayesian optimal experimental designs for models whose factor settings are functions, known as profile factors. This type of experiments which involve factors that vary dynamically over time, presenting unique challenges in both estimation and design due to the infinite-dimensional nature of functions. The package fdesigns implements a dimension reduction method leveraging basis functions of the B-spline basis system. The package fdesigns contains functions that effectively reduce the design problem to the optimisation of basis coefficients for functional linear functional generalised linear models, and it accommodates various options. Applications of the fdesigns package are demonstrated through a series of examples that showcase its capabilities in identifying optimal designs for functional linear and generalised linear models. The examples highlight how the package's functions can be used to efficiently design experiments involving both profile and scalar factors, including interactions and polynomial effects.

stat.CO↗