One of the most important areas of medical inquiry is the identification of risk factors for specific disorders. Such research is usually aimed at discovering new causes of a disease, but risk factors can also be used as screening tests. The fact that a risk factor must be very strongly associated with a disorder if it is to be a worthwhile screening test is not widely recognised. If this were better understood, fewer risk factors would be proposed unnecessarily as screening tests. Serum cholesterol measurement, for example, would probably never have been considered seriously as a screening test for ischaemic heart disease. Although a high cholesterol concentration is a strong risk factor for ischaemic heart disease in aetiological terms, the association is not sufficiently strong for it to be used as a screening test—in practice, its screening performance is poor.1

In this article we specify the quantitative relation between risk factors and screening tests and show how strongly a risk factor needs to be associated with a disease before it is likely to be a useful screening test. For simplicity, we consider only risk factors with a Gaussian distribution, though the general principles we present can be applied to all frequency distributions.