To compensate for the missing values in the logistic regression models, multiple imputation was employed using multivariate normal regression, based on age, sex, and smoking data; multiple imputation results in less biased findings when dealing with missing covariate data [ 13].
The SADPM (14) is a multivariate logistic regression based on age, sex, BMI, ethnicity, FPG, HDL cholesterol, and blood pressure to compute a risk score for future T2DM.
A risk score for a P-Ca analysis was created with stepwise logistic regression [ 11] based on age, concomitant diagnosis and drug treatment, in order to control for confounding factors.
BMI was imputed where it was unavailable using a regression imputation technique based on age, gender, receipt of pension, systolic and diastolic blood pressure and index of relative disadvantage.
We developed a regression formula for calculating ID and OD based on age in months and calculated the coefficient of determination R2 by using a regression analyses.
The regression formula for predicting OD based on age in months was OD = 0.024 × age in months + 5.21, and coefficient of determination R2 was 0.558.
The regression formula for predicting ID by based on age in months was ID = 0.019 × age in months + 3.48, and the coefficient of determination R2 was 0.54.
The linear regression equations did not differ significantly across subgroups based on age, sex, diabetes type, race/ethnicity, or smoking status.
Multiple regression was used for imputation of missing data with predictive values based on age, sex, race, and adherence.
Linear regression models were used to predict the lung function parameter FEV1 and FVC based on age, height, race and sex.
