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Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters by Grantham, Kelsey L. (author);Kasza, Jessica (author);Heritier, Stephane (author);Carlin, John B. (author);Forbes, Andrew B. (author) is a Mathematics article available to read on EtoBox.
What is Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters about?
## Background Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters. Small-sample methods such as the Kenward-Roger approximation in combination with REML can potentially improve estimation of the fixed effects such as the treatment effect. A Bayesian approach may also be promising for such multilevel models but has not yet seen much application in cluster randomized trials. ## Methods We conducted a simulation study comparing the performance of REML with and without a Kenward-Roger approximation to a Bayesian approach using weakly informative prior distributions on the intracluster correlation parameters. We considered a continuous outcome and a range of stepped wedge trial configurations with between 4 and 40 clusters. To assess method performance we calculated bias and mean squared
Who reads Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- Grantham, Kelsey L. (author);Kasza, Jessica (author);Heritier, Stephane (author);Carlin, John B. (author);Forbes, Andrew B. (author)
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Mathematics (Physical Sciences)