Adjusting for confounded variables: Pulmonary function and smoking in a special population

Abstract

Confounded variables present an obstacle to valid inference in many environmental and occupational studies. We describe a series of procedures that we used to address this problem in a study of pulmonary function and smoking. Subjects were drawn from the Multiple Risk Factor Intervention Trial (MRFIT), a prospective study of coronary heart disease. Confounding of smoking, hypertension, and hyperlipidemia was designed into the trial and was beyond the control of our ancillary study. We used statistical techniques to detect and characterize the pattern of confounding, identify important variables affecting pulmonary function, and perform appropriate adjustments for extraneous influences (i.e., other than smoking). Among the techniques we used were factor analysis, stepwise multiple regression, and bootstrap replication. Analysis of the adjusted pulmonary function measurements showed that they were satisfactorily standardized and free of artifact. Moreover, use of the adjusted values sharpened our statistical results concerning smoking, the ultimate object of the study. We contrast the use of external and internal standards and discuss methods for detecting, ruling out, or counteracting confounding.