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Our measurement error correction method was designed to correct for the most likely source of error in long-term pollution studies, namely differences between ambient and personal levels of PM2.5 of ambient origin.
SEs for the EC and OC health effects were virtually unchanged when measurement error correction was implemented, whereas the bootstrap-corrected SEs for Si and S were about 50% larger than their respective naïve estimates.
Unfortunately, in time-series studies of air pollution, there are no available calibration data (routinely measured series of personal exposure data) on which to base a measurement error correction.
The results from the naïve health model that did not include any measurement error correction, as well as the results from the health model that included bootstrap-corrected point estimates and SEs of ^βx, are displayed in Table 5.
The methods for measurement error correction are illustrated using a study of the effect of in utero lead exposure on infant birth weight.
Measurement error correction.
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Simulation studies have found that measurement error corrections are accurate when β1σ < 0.5 (Kuha 1994).
As mentioned, the measurement error corrections did not alter the initial findings much.
In addition, they highlight variation in measurement error by pollutant and support the implementation of measurement error corrections when possible.
We conducted measurement error corrections for the association between total fat intake and risk of ovarian cancer using the calibration substudy data.
It is dangerous to take measurement error corrections derived from one study and apply them to data from entirely different studies or populations.
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