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Generalized estimating equations were used to help account for clustering of data within hospitals.
Summary estimates of compliance, and of associations between compliance and features of study conduct, were computed allowing for clustering of data within studies.
However, our modeling techniques account for clustering of data and moreover, we found that our findings were robust even when we dropped the records of siblings with identical birthweight.
We used logistic regression (PROC GENMOD in SAS v.9.1), which accounted for clustering of data both within-reader and within-scan, allowing data from both occasions to be used, to compare the effect of reader, scan and patient characteristics on the probability of agreeing with the reference standard about the detection of any early ischaemic signs without and then when using a scale.
Statistical analyses were descriptive with adjustment for clustering of data.
Participants were treated as random effects (to allow for clustering of data within each participant).
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Look for clusters of data.
Similarly, memory requirements increase with the number of data points; while clustering of 10,000 data points required 0.5 GB of RAM, memory consumption grew up to 2 GB for clustering of 20,000 data points.
We conducted multilevel analyses to adjust for clustering of the data.
Hierarchical clustering [ 57, 58] is the widely used algorithm for clustering of multivariate data.
Bayesian models for clustering of genotype data use the framework of mixture models to model individuals.
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