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Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization.
The collection of high-quality clustered data for research may thus be a useful side effect of geographical containment.
Since each of the participants valued 6 health states, we used the Eicker-Huber-White robust standard error for clustered data for statistical inference [ 14].
Gaining insight into bootstrapping clustered data for all these methods and draw comparison to our proposed likelihood based approach warrants serious investigation and is beyond the scope of this paper.
We used SUDAAN software, 19 which is designed for the analysis of clustered data, for all data analysis, treating each school as a cluster and each student's multiple responses as subclusters in the analyses.
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Clustering data for the CC group revealed three clusters.
Analyses were rerun univariately using the non-parametric Wilcoxon rank test for clustered data [ 21] for those outcome variables where the assumption of a normal distribution was rejected, and conclusions were compared with those based on the parametric regression models.
Random effects models (also known as 'mixed models') and Generalised Estimating Equations (GEE) are two popular approaches for analysing clustered data; they account for clustering and provide, respectively, cluster-specific (conditional) and population-average (marginal) inference [ 2].
We used robust variance estimation suited to longitudinal or clustered data to account for possible clustering from outbreaks.
Risk factors for SIEs were investigated by logistic regression models for clustered data (Table 3).
Appropriate statistical analyses for clustered data must be adopted.
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