At enrollment, for both initial unconditional logistic regression (data not shown), and proportional odds modeling (Table 2), AHS swine-exposed and AHS nonswine-exposed participants had markedly higher distributions of antibody titers against both swine influenza viruses compared to university controls.
Both conditional and unconditional logistic regression models based on the full dataset are presented adjusting for the following known endometrial cancer risk factors: education, menopausal status, age at menarche, parity, oral contraceptive use, hormone replacement therapy use, body mass index, smoking, history of high blood pressure, and history of diabetes.
Incorporating both SNPs into an unconditional logistic regression model showed that neither of the pair of SNPs fully captured the association signal in the region [odds ratio (OR) = 1.06, P = 1.06 × 10−4 for rs6691170 and OR = 1.07, P = 2.48 × 10−4 for rs6687758].
Odds ratios and 95% confidence intervals were estimated using unconditional logistic regression in both unadjusted and adjusted models.
Table 4 shows the unconditional logistic regression models, both crude and adjusted, for risk of high-grade dysplasia by telomere length.
Two groups of unconditional logistic regression models both for overweight and hypertension (SBP≥140 mmHg or DBP≥90 mmHg) were constructed adjusting for well-known determinants.
Statistical analyses included exact χ2 tests, independent sample t tests, and unconditional logistic regression.
Odds ratios for ovarian cancer by number of LOC were estimated using unconditional logistic regression.
Unconditional logistic regression analyses were done to identify predictors of enrolment.
Odds ratios were calculated using unconditional logistic regression for categories of NSAIDs and colon cancer risk.
