Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters

• Published in: Journal of applied statistics Vol. 48; no. 9; pp. 1527 - 1540
• Main Authors: Singh, Satya Prakash, Yadav, Pradeep
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 04.07.2021; Taylor & Francis Ltd
• Subjects: Bayesian design; cluster-randomized trials; Clusters; Design parameters; efficiency; Heterogeneity; Households; matched pair clusters; optimal design; Optimization; Parameter estimation; power; Statistical methods; optimal design; efficiency; Bayesian design; cluster-randomized trials; matched pair clusters; power
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2020.1779195

In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the individuals. It results in the loss of efficiency in terms of the estimation of the unknown parameters as well as the power of the test for testing the treatment effects. To recoup this efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, the clusters within a treatment arm might be heterogeneous in nature. In this article, we propose a locally optimal design that accounts the clusters heterogeneity and optimally allocates the subjects within each cluster. To address the dependency of design on the unknown parameters, we also discuss Bayesian optimal designs. Performances of proposed designs are investigated numerically through some data examples.