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Since screening effects are likely to vary by age and time since screening, these variables may not be balanced between comparison groups.
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We screened these variables, and excluded from further analysis those with more than 5% missing values.
Plackett Burman design was employed for screening the variables significantly affecting the extraction efficiency.
The statistical methods of screening the variables also differed between studies.
Consistent with strong correlation with screening status, these variables were associated with increased detection of early prostate cancers and reduced detection of advanced prostate cancers.
A 24 full factorial design analysis was performed to screen the variables affecting Pb II) removal efficiency.
Simple batch experiments and a 25 factorial experimental design were employed to screen the variables affecting Cr VI) removal efficiency.
R-package was used to screen the variables via the library 'randomForest' [22].
This was done to screen the variables for later inclusion in a multivariable model.
A multivariable analysis was used to screen the variables within each subset.
A liberal P-value of ≤0.2 was set to be significant to pre-screen the variables.
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