## The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable.

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**The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable.** / Ditlevsen, Susanne; Christensen, Ulla; Lynch, John; Damsgaard, Mogens Trab; Keiding, Niels.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

#### Harvard

*Epidemiology*, bind 16, nr. 1, s. 114-120.

#### APA

*Epidemiology*,

*16*(1), 114-120.

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#### RIS

TY - JOUR

T1 - The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable.

AU - Ditlevsen, Susanne

AU - Christensen, Ulla

AU - Lynch, John

AU - Damsgaard, Mogens Trab

AU - Keiding, Niels

N1 - Keywords: Adult; Age Factors; Algorithms; Denmark; Female; Humans; Interferon-alpha; Longitudinal Studies; Macular Degeneration; Male; Middle Aged; Models, Theoretical; Socioeconomic Factors; Treatment Outcome

PY - 2005

Y1 - 2005

N2 - 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-alpha 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.

AB - 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-alpha 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.

M3 - Journal article

C2 - 15613954

VL - 16

SP - 114

EP - 120

JO - Epidemiology

JF - Epidemiology

SN - 1044-3983

IS - 1

ER -

ID: 5397866