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Students' pre-test and post-test are treated as matched variables.
This matching procedure was designed to minimize differences in clinical course and symptoms that could be attributable to the matched variables.
We used conditional logistic regression for the matched community controls and these analyses were performed with and without additional adjustment for sex (in addition to the matched variables age and place of living).
The only factors that were significantly related to ACS case status were HDL-C (OR = 0.56, 95% CI 0.43 to 0.71) and smoking status (OR = 2.86, 95% CI 1.79 to 5.07; age and sex were not predictive because they were matched variables).
*Adjusted for matched variables: sex, age, and calendar year.
Serial calcium scores calculated for reversibility assessment were compared using dependent t-test for matched variables.
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By matching, the ability to study the matched variable directly is lost.
Then, we used SAS MACRO to identify hypoglycemic diabetic patients with the same matched variable and randomly selected one to four nonhypoglycemic diabetic cases from those with the same match variables.
As cases and controls will be frequency-matched, unconditional logistic regression with matching variables as covariates will be employed.
High risk data had a two part validation system to match variables across databases.
The availability of observable covariates that serve as matching variables critically influences the internal validity of the matching.
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