ObjectiveTo investigate the importance of controlling confounding factors during
binary logistic regression analysis.MethodsMale coronary heart disease (CHD) patients (n = 664) and healthy control
subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric
acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low
density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100,
lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect
bilirubin, and γ-glutamyl transferase. Associations between these indexes
and CHD were assessed by logistic regression, and results were compared by
using different analysis strategies.Results1) Without controlling for confounding factors, 14 indexes were directly
inputted in the analysis process, and 11 indexes were finally retained. A
model was obtained with conflicting results. 2) According to the application
conditions for logistic regression analysis, all 14 indexes were weighed
according to their variances and the results of correlation analysis. Seven
indexes were finally included in the model. The model was verified by
receiver operating characteristic curve, with an area under the curve of
0.927.ConclusionsWhen binary logistic regression analysis is used to evaluate the complex
relationships between risk factors and CHD, strict control of confounding
factors can improve the reliability and validity of the analysis.