ArticleStatistics and computing2023
Evaluation of combinatorial optimisation algorithms for c-optimal experimental designs with correlated observations.
Article in Statistics and computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Optimal Allocation of Observations in Stepped-Wedge and Other Cluster Studies With Correlated Cluster-Period Effects.Statistics in medicine · 2025Article
- Optimal study designs for cluster randomised trials: An overview of methods and results.Statistical methods in medical research · 2023Review
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2 authors.
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Abstract
We show how combinatorial optimisation algorithms can be applied to the problem of identifying c-optimal experimental designs when there may be correlation between and within experimental units and evaluate the performance of relevant algorithms. We assume the data generating process is a generalised linear mixed model and show that the c-optimal design criterion is a monotone supermodular function amenable to a set of simple minimisation algorithms. We evaluate the performance of three relevant algorithms: the local search, the greedy search, and the reverse greedy search. We show that the local and reverse greedy searches provide comparable performance with the worst design outputs having variance Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-023-10280-w.
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