{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["22(1)"],"submitter":["Grantham KL"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h"],"journal":["BMC medical research methodology"],"pagination":["112"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9009029"],"repository":["biostudies-literature"],"pubmed_title":["Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters."],"pmcid":["PMC9009029"],"pubmed_authors":["Forbes AB","Kasza J","Grantham KL","Heritier S","Carlin JB"],"additional_accession":[]},"is_claimable":false,"name":"Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters.","description":"<h4>Background</h4>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.<h4>Methods</h","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2025-04-26T07:24:33.958Z","creation":"2025-04-06T12:18:12.007Z"},"accession":"S-EPMC9009029","cross_references":{"pubmed":["35418034"],"doi":["10.1186/s12874-022-01550-8"]}}