A discussion of some statistical methods for separating within-pair associations from associations among all twins in research on fetal origins of disease.

Twin data can be used to gain insights into the origin of associations between factors arising in fetal life and the risk of later disease. This is because twin data afford an opportunity to conduct paired analyses that take the influence of shared factors into account. When an association that is present in an unpaired analysis is present also in a paired analysis, there is evidence that the causal pathway linking the fetal factor and the disease may have a fetal origin. If the association disappears in the paired analysis, there is evidence that it may have has arisen from a shared source such as the mother. The relevant factors include diet and socio-economic status. There are several statistical approaches to this. The simplest involves comparing, say, a coefficient from a regression of an outcome on a fetal factor for all subjects in a twin sample, with the coefficient obtained from regressing the within-pair difference in the outcome on the within-pair difference in the fetal factor. Alternative approaches involve simultaneously estimating regression parameters for between- and within-pair components. These approaches permit similar inferences about whether the association is due to individual (fetal) or shared (maternal) factors, and are valid in the circumstances that non-shared factors missing from the regression model do not influence the regression estimates.