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Confounding is recognized as a mixing of effects that can lead to spurious conclusions about the association between disease and a putative risk factor. Confounding occurs if an extraneous factor causes disease and is associated with the exposure of interest. Since information on potential confounders may be missing, the investigator may assess confounding indirectly by specifying values for three types of parameters: the prevalence of the covariate in the population, the association between exposure and the covariate, and the effect of the covariate on disease. Qualitative and quantitative arguments suggest that adjustment for a potential confounder may have small effects on the risk ratio, even if the confounder is a strong risk factor. In this report we illustrate graphically the effect that adjustment for a confounder will have on the risk ratio and derive limits for the magnitude of that effect. Our approach allows the investigator to calculate limits for the maximum effect of covariate adjustment, even if only one or two of the relevant parameters can he specified.