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As in other observational studies, dietary data measured by FFQs are subject to measurement error; however, due the prospective design, misclassification is likely to be nondifferential, which is likely to bias results toward the null.
The non-differential measurement error, however, would lead the results toward the null values.
Food-frequency questionnaires involve a certain degree of measurement error; however, the ability to allocate subjects into a broad dietary pattern is probably very strong.
This is probably a result of the complex movement in the knee joint (superimposition of different movements); a measurement error however cannot be excluded.
We cannot exclude the possibility of measurement error; however, in our validation study, the correlation coefficient was higher for unprocessed compared with processed red meat.
The adjusted and two-stage models were essentially robust to measurement error; however, both the unadjusted and adjusted did not always produce unbiased estimates of parameters for all underlying DAGs, especially at different levels of α.
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Mendelian randomization could help distinguish causal effects from artifacts due to residual confounding and measurement errors; however, polymorphisms reliably associated with alcohol phenotypes are needed.
However, the measurement error can still be assumed to be included in the multilevel model specification (i.e., the second term on the right-hand side).
The non-uniqueness and measurement errors may however make this electromagnetic inversion problem strongly ill-posed, leading to potentially inaccurate saturation profiles.
The effect of such measurement errors is, however, diminished since we are dealing with ratios rather than prevalences.
The ordinal scale exposure variable per the use of maternal report of fuel type, and cooking and garbage burning practices was sensitive to certain amount of measurement error but was however not likely to be related to the outcome of interest.
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