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

Singh, Satya Prakash; Yadav, Pradeep K.

doi:10.1080/02664763.2020.1779195

Cited by 2 publications

(1 citation statement)

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“…Based on hypothetical as well as real scenarios, it is shown that the proposed design is more powerful compared to the commonly used balanced design. The required sample size to achieve a prespecified power can be done using an iterative procedure as described in Singh and Yadav (2021).…”

Section: Summary and Discussionmentioning

confidence: 99%

Bayesian optimal stepped wedge design

Singh

2023

Biometrical J

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Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster–crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in design and analysis of stepped wedge trials. In this paper, we discuss a Bayesian approach to address the dependency of SWD on the ICC and robust Bayesian SWDs are proposed. Bayesian design is shown to be more robust against the misspecification of the parameter values compared to the locally optimal design. Designs are obtained for the various choices of priors assigned to the ICC. A detailed sensitivity analysis is performed to assess the robustness of proposed optimal designs. The power superiority of Bayesian design against the commonly used balanced design is demonstrated numerically using hypothetical as well as real scenarios.

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Section: Summary and Discussionmentioning

confidence: 99%