… Conclusion We recommend that CRTs with ≤ 30 clusters and a binary outcome use an …
or restricted pseudolikelihood GLMM both with DoF clusters minus cluster-level parameters. …

… In summary we recommend that SW-CRTs with a limited number of clusters and binary
outcomes should be analysed using a GLMM. Our strongest recommendation of all is that a cross-…

… , and make recommendations regarding if and when clustering should be taken into … on
binary outcomes, such as mortality and morbidity, we also restrict our attention to such outcomes, …

… We let Yijl denote the binary outcomes for the lth subject in the ijth cluster and Yij denote the
total number of positive responses in the ijth cluster. ˆPij =… Number of Mean number clusters …

… for the analysis of independent binary data after a small amount of preprocessing. The …
clustered binary data: testing homogeneity of proportions, estimating dose-response models and …

… different assumptions regarding the between-cluster variability in the intervention arm of a …
for binary outcome data between two common measures of between-cluster variability: k, the …

… Convergence can, however, be a problem for multi-level models with binary data. Also, if
there are many clusters with small number of units, thus having some clusters where all …

… the number of clusters is not large, and the sizes of these clusters are on a smaller scale than
… outcomes are binary indicators of a desired outcome (success), while allowing for possible …

… most appropriate for binary outcomes from … binary outcome. We consider both models that
estimate a treatment odds ratio (OR), including hierarchical models and GEEs, and also cluster…

… The validity of this assumption is di cult to assess, particularly when the number of clusters
is small, as it is in many cluster randomized trials. A standard method of checking for normality …