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DEEP is an adaptive method that dynamically designs elicitation questions for estimating Risk and Time preference parameters.
During that time, we've learned a lot about alcohol and cancer, and powerful statistical methods have been developed for estimating risk.
Although techniques for estimating risk over increasingly large spatial scales are becoming more widespread, the connection of risk assessments from broad to fine scales is not well established.
However, given the quality and variability of the data, human studies alone, especially those involving sensory irritation, are not adequate to serve as a reference concentration for estimating risk, or lack thereof, for a lifetime of exposure to formaldehyde.
However, the critical issue is in the application or use of the historical dataset as the basis for estimating risk.
By incorporating these variables, we developed a nomogram, which provides a highly accurate means for estimating risk of BCR after RP.
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There was no data available for estimating risk-group dependent need to re-treat differently excavated teeth.
For estimating risks, we employed OR with a 95%% CI.
For estimating risks, the margins of exposure (MOEs) were determined.
The BEIR VI risk model was useful for estimating risks to miners and, by downward extrapolation, for estimating risks to the general population from indoor radon.
For estimating risks, we employed odds ratio (OR) with a 95% confidence interval (95% CI).
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