It is often of interest to assess how much of the effect of an exposure on a response is mediated through an intermediate variable. However, systematic approaches are lacking, other than assessment of a surrogate marker for the endpoint of a clinical trial. We review a measure of “proportion explained” in the context of observational epidemiologic studies. The measure has been much debated; we show how several of the drawbacks are alleviated when exposures, mediators, and responses are continuous and are embedded in a structural equation framework. These conditions also allow for consideration of several intermediate variables. Binary or categorical variables can be included directly through threshold models. We call this measure the mediation proportion, that is, the part of an exposure effect on outcome explained by a third, intermediate variable. Two examples illustrate the approach. The first example is a randomized clinical trial of the effects of interferon-α on visual acuity in patients with age-related macular degeneration. In this example, the exposure, mediator and response are all binary. The second example is a common problem in social epidemiology—to find the proportion of a social class effect on a health outcome that is mediated by psychologic variables. Both the mediator and the response are composed of several ordered categorical variables, with confounders present. Finally, we extend the example to more than one mediator.
From the *Department of Biostatistics, †Department of Social Medicine, and §Department of Social Medicine, Institute of Public Health, University of Copenhagen, Denmark; and ‡Department of Epidemiology, University of Michigan, Ann Arbor.
Supplemental material for this article is available with the online version of the Journal at www.epidem.com.
Submitted 21 November 2002; final version accepted 16 September 2004.
Supported by the Danish Research Councils (Ref. no. 9801268).
Correspondence: Susanne Ditlevsen, Department of Biostatistics, Panum Institute, Blegdamsvej 3, DK-220, Copenhagen N, Denmark. E-mail: S.Ditlevsen@pubhealth.ku.dk