Exact(1)
Optimized cutoffs were defined using logistic regression models, where diagnosis was the response variable and a binary classifier (biomarker < cutoff) was the predicting variable (models adjusted for age and sex).
Similar(7)
j Birthweight (as a continuous variable) model adjusted for age same variables as LBW model.
A multiple variable model adjusting for covariates estimated prevalence ratios (PR) of having good to excellent SRDH and included an interaction term for insurance and household income group.
After adjustment, both for multivariate analysis (BMI as continuous variable) or models adjusted for various covariables (BMI groups), significant relation between lower BMI and worse survival was found in 15 out of 25 studies.
We also found that the prevalence of SGA was significantly associated with atrazine when this was included as a continuous variable in the models (adjusted PR = 1.15; 95% CI, 1.04 1.28).
Regional atrophy rates (dichotomized into high and low rates of atrophy, based on the median) were used as independent variables in separate models, adjusted for age and sex.
Linear regression models were constructed with HRBS drug and sex sub-scores as the dependent variables and gender as the independent variable; models were adjusted for demographic variables followed by potential mediators selected a priori that were associated with both the independent and dependent variables at a level of statistical significance of p < 0.05 (Baron and Kenny 1986).
Instrumental variable models provide adjusted effect estimates at one time point and on an absolute rather than a relative scale.
Related(1)
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