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Fourthly, our key model estimates represent current practice.
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The distributions of the exposure concentrations, the DNA damage, and mRNA expression were skewed; therefore all statistical analyses were performed on the natural logarithm of these data, with model estimates representing slopes on the logarithmic scale.
Thirdly, unless explicitly separated, model estimates may represent a compound of within and between individual sources of information [ 30].
In the linear regression model presented in Table 2, the estimates represent the change in mean levels of each outcome variable associated with a 1-μg/L increase in plasma Se with and without adjusting for age, sex, BMI, smoking, betel nut use, plasma folate, plasma B12, urinary creatinine, and water As.
These estimates represent each variable entered into separate models (i.e., not mutually adjusted exposure models).
Even in the linear case, under heterogeneity in the exposure outcome model, the ratio estimate represents an average causal effect across the population.
Table 2 presents the parameter estimates, SEs, and p-values for these models, with the ®-coefficient estimate representing the mean difference in handwipe PBDEs on the natural log scale.
Regardless, we concluded that the shift from OP exposure through multipathways to dietary ingestion exclusively has made the PBPK model a valuable tool for converting dietary pesticide exposure to estimate absorbed dose estimates when model input data represent the best estimates of dietary-pesticide exposures.
Double open circles show the values of the optimum model and dots represent estimates of bootstrap tests.
We used a fixed-effects model to represent overall estimate effects.
The fixed effect for smoking, in the random effect model, for example, represents an estimate that is conditional on the random effect.
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