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We chose to examine fine rather than coarse particles because, with only one monitor in each district, exposure misclassification error would likely be lower.
Similar(59)
If the estimation algorithm for a model with a random effect were to successfully converge, standard errors would likely be markedly bigger.
Such error, however, would likely be non-differential thus resulting in conservative estimates of associations.
That is, investigators using our approach with the improved read data would almost certainly not have such assembly errors because 1) there would likely be no such regions since data output is so high and 2) the accuracy of individual reads is so much higher.
There may be some degree of measurement error in CIMT, but this would likely be nondifferential with respect to exposure.
Because the PVT-192 is a specialized real-time system, it would be expected that the error in the PC-derived data would likely be worse.
In this case, the mean visits and standard errors differ significantly between these categories so information would likely be lost by this aggregation.
If an error is charged to the outfielder, then the batter would likely be credited with either a double or triple.
For studies where the cohort is located much further from monitoring locations, this would likely be a larger source of error.
Therefore, assuming such measurement errors were nondifferential with respect to the populations at risk, the effect estimates would likely be biased downward.
Still, the acceptance would likely be grudging.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com