arXiv subjects

Alan Girling

Publications and source records attributed to Alan Girling.

2 recordsLinked to original sources

arXiv2026

Optimal designs for incomplete stepped wedge trials

Background: Stepped wedge trials are longitudinal randomised evaluations, usually cluster-randomised, in which the experimental intervention is introduced in a staggered fashion. Incomplete stepped wedge designs focus the effort of data collection on particular periods in particular sequences. Methods: We suppose there is a cost for every period in every cluster where we collect data, and that there are a fixed number of individuals, m, with data available in each period in each cluster. If we are willing to pay the cost of data collection in that cluster-period then we collect the data on all m individuals, and if we are not willing to pay the cost then we collect no data in that cluster-period. We consider the problem of designing a trial to minimise the total number of cluster-periods of data collection needed to achieve given precision for the treatment effect estimator, or equivalently, to maximise precision for a given number of cluster-periods of data collection. Results: We present the solution for two-period trials, which has two distinct forms, depending on the correlation between two cluster-period means from the same cluster in different periods. We also present a conjecture on the form of the solution for multi-period trials, informed by results from a greedy search of the design space. Conclusions: A real-life stepped wedge design problem will involve trading off the costs of various design elements subject also to constraints on the scale of data collection. Nevertheless, the solutions to the problem considered here add significantly to our understanding of the optimal design of incomplete stepped wedge trials.

stat.AP↗

arXiv2023

Optimal Study Designs for Cluster Randomised Trials: An Overview of Methods and Results

There are multiple cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at that time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding weights to whole numbers of clusters and extensions to non-Gaussian models. We present results from multiple cluster trial examples that compare the different methods, including problems involving determining optimal allocation of clusters across a set of cluster sequences, and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures.

stat.ME↗