Effects of the use of unreliable surrogate variables on the validity of epidemiologic research studies

When certain key factors of interest in epidemiologic research studies cannot be measured directly, epidemiologists often turn to the use of surrogate variables. The potential bias in making statistical inferences about an adjusted exposure-disease association parameter (e.g., a partial correlation) is described as a function of the degree of unreliability in the surrogate variables used in place of the underlying disease, exposure, and confounding factors of real interest. It is shown that unreliability in the surrogate confounder is much more apt to produce seriously misleading inferences than is unreliability in the surrogate measures for disease and exposure. Practical methods are discussed for dealing with less than perfectly reliable surrogate variables.