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Differential misclassification could be a source of bias in the present study, yielding spurious associations between exposure and outcome.
Misclassification could be recording the wrong category for an ordinal or nominal covariate, or measurement error for a continuous covariate.
Additional exposure misclassification could be attributed to the extrapolation of predictions from the census-tract level to the county level.
Any exposure misclassification could be reduced if the number of measurement sites could be increased, making it easier to evaluate local variations in pollution levels.
Exposure misclassification could be attributable to the time spent in different environments, although we think that it is unlikely to be differential among cases and controls.
We believe that each of these avenues of exposure misclassification could be non-differential and that the likely consequences are effects that are biased towards the null.
Similar(45)
Alternatively, the misclassification rate could be minimised.
Other loss functions such as the L1-loss function or the misclassification rate could be implemented in an easy manner.
Finally, a misclassification bias could be present in our study, since patients with only one reported episode could have had another episode prior to our study time period.
The resulting misclassification bias could be further exacerbated if the process of community engagement leads to social desirability in the responses of individual FSWs to questions on empowerment.
A misclassification bias could be happen when analyzing the present data because some lowly socio-ecological leveled or educated people didn't know and use the welfare of the health insurance system.
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