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Since each classified exposure covers a relatively large spectrum of ground roughness with constant indices such as exponent α, this results in uncertain wind-resistant structural analysis and design for structures in each and every categorized exposure.
We did also estimate p-values for trend by including the categorized exposure variables as continuous variables (coded 0, 1, 2 and 3) in the models.
The design of this study was similar to previous studies, although one potentially important difference is that we estimated the effect of air pollution as a continuous exposure (as did [24], [25]), whereas other studies categorized exposure into quartiles [22], [23].
We categorized exposure using a priori cut points at the 50th and 90th percentiles.
For this study, we categorized exposure groups using mean individual air benzene measurements obtained during the 3 months preceding phlebotomy.
We found no statistically significant associations in Swedish fisher-man, using either continuous or categorized exposure variables.
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We categorized exposures of interest into quartiles based on their distributions.
Second, we categorized exposures into quintiles and estimated differences in mean values for the 2nd, 3rd, 4th, and 5th quintiles compared with the first quintile.
Serum PCBs were log-transformed and entered both as continuous and categorized exposures along with birth weight (grams) and covariates [smoking (yes/no), height (inches), and infant sex (male/female)] into linear regression.
When we categorized exposures into "low risk" lifestyle factors, normal weight before pregnancy, regular exercise, healthy diet, and not smoking were all significantly and independently associated with a lower risk of gestational diabetes (table 2).
Previous studies used several different methods of categorizing exposure.
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