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With covariates, it can be estimated using logistic regression.
The probabilities can again be estimated using logistic regression.
Crude odds ratios (ORs) for quitting will be estimated using logistic regression model and were compared with ORs that were adjusted for baseline variables (e.g. sex, age, education, stage of readiness).
A non-time-varying association between a time-varying exposure and an outcome can be estimated using logistic regression by fitting separate models within a series of time periods, using current values of the time-varying exposure in each period, and pooling the estimates across periods.
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Univariable and multivariable odds ratios were estimated using logistic regression with the outcome of interest being MGD (IGT, GDM and type 2 diabetes).
Risk of SBD through job stress was estimated using logistic regression with odds ratios (ORs) and 95% confidence intervals (CIs) as measures of association.
The associations of anxiety and depressive symptoms as well as behavioral problems at baseline with headache status at follow-up were estimated using logistic regression.
The effects of major roads and services, residents' attraction to areas of high natural amenity, and previous residential development were estimated using logistic regression models and geographically weighted logistic regression models, respectively.
Risk was estimated using logistic regression.
Receipt of infectious disease testing was estimated using logistic regression.
ORs with 95% CIs were estimated using logistic regression.
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