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Like the naïve MH approach, this cluster adjustment does not distinguish between informative and incidental clustering.
An example of incidental clustering might be an association between male absent households and food insufficiency.
The MH adjustment assuming incidental clustering generated a final model very similar to GEE.
Multilevel analysis attempts to distinguish between informative and incidental clustering [ 7- 9] – sometimes called compositional and textual explanations [ 10].
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fMRI analysis of incidental encoding demonstrated clusters of activation in para-hippocampal regions and in the hippocampus in HC and MCI patients but not in AD patients.
This adjustment assumes, however, that the clustering is incidental to the association.
Both cluster adjusted MH and GEE presume that all clustering is incidental – the former by assuming a fixed OR across clusters and the latter by largely ignoring differences between clusters.
Pivotal to analysing clustered data is the understanding that clustering can be informative or incidental.
On the other hand, clustered nests survived better when incidental skunk-like predators were present, but survival was dramatically reduced in the presence of foxes; thus, small changes in the predator community (e.g. introduction of foxes) without commensurate changes in nest clustering could have important effects on waterfowl populations.
Articles about incidental findings and cognitive enhancement form relatively cloistered clusters.
The salient neuroethics terms used to translate the machine-derived labels for each cluster were: decision making, cognitive enhancement, personhood, incidental findings, legal implications, minimally conscious states, truth telling, and the term "neuroethics" itself.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com