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