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Point and interval estimates of odds ratios were obtained by fitting conditional logistic regression models for matched sets.
We evaluated the association between mammographic density and breast cancer risk by fitting conditional logistic regression models, adjusting for machine (due to different sampling ratios of cases and controls), with the mammographic density measures as both continuous and categorical variables.
We estimated the risk of the four different outcomes associated with dispensation of any DPP-4i by fitting conditional logistic regression models, expressed as ORs, and corresponding 95% CIs.
Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by fitting conditional logistic regression models using all women or subgroups of women defined by menopausal status and hormone therapy use.
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We then calculated cumulative doses and fitted conditional logistic regression models conditioning on case controls sets.
The PROC PHREG steps fit conditional logistic regression models.
We fitted conditional logistic regression models to examine the associations between SHBG and total testosterone levels with diabetes by sex.
We fitted conditional logistic regression models to estimate the association between SHBG and diabetes.
We conducted all analyses with SAS software (version 9.2; SAS Institute Inc., Cary, NC) using PROC LOGISTIC to fit conditional logistic regression models.
Using this method and exposure definition, we fitted conditional logistic regression models and estimated odds ratios (ORs) relating exposure status to a diagnosis of schizophrenia spectrum disorder.
Before testing for proximity, we fitted conditional logistic regression models with a term for social class, as inferred from the father's occupation recorded on the birth certificate.
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