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2 and its subtypes were estimated from unconditional logistic regression models with adjustment for confounders.
3 Associations were examined in linear and logistic regression models with adjustment for demographic and comorbi
4 e intervals (CI) were calculated using conditional logistic regression models with adjustment for important covariates ex
5 : 0.88, 1.02 (P = 0.16), respectively] in multiple logistic regression models with adjustment for potential confounders.
6 liance were then evaluated in a series of multiple logistic regression models with adjustment for potential confounders.
7 aluated as predictors of tooth loss using negative binomial regression models with adjustment for potential confounding f
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