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Although misclassification is difficult to assess, the results suggest that bias due to reporting errors could be greater than bias caused by nonresponse.
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Ideally, this group should consist of non-LD CAP patients only, but misclassification was difficult to avoid because of the epidemiological situation and the low negative predictive value of a negative UAT.
The extent and influence of potential misclassification bias is difficult to ascertain.
We focused on all-cause death since determination of causes of death is prone to misclassification and is difficult to perform, even if centrally adjudicated [ 14].
Although this approach has been used extensively in analysis of administrative data, the direction and extent of misclassification introduced is difficult to estimate.
Thedirection of bias that might have resulted from misclassification of arthritis status is difficult to anticipate.
The impact of misclassification on departures from additive effects is difficult to predict [ 9, 10].
Adjustment for misclassification would be difficult, as denial rates are likely to vary by aspects of the study design, the way questions are asked, and also by sex, age, location and other demographic variables.
Likewise, our use of self-reported health-behaviors data including cigarette smoking, alcohol consumption and prenatal vitamin use may have introduced misclassification, but the nature of any misreport is difficult to predict.
It is difficult to predict how this misclassification would bias our results.
Generally non-random misclassification leads to underestimation of ORs, while differential misclassification might have overestimated the ORs in this study; it is difficult to evaluate the overall magnitude of information bias in this study.
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