International Journal of Epidemiology 2002;31:163-165
© International Epidemiological Association 2002
Theory and Methods |
Fallibility in estimating direct effects
a Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA.
b Department of Epidemiology, Harvard School of Public Health, MA, USA.
Dr Stephen Cole, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St E-7139 Baltimore, MD 21205, USA. E-mail: scole{at}jhsph.edu
Abstract
We use causal graphs and a partly hypothetical example from the Physicians' Health Study to explain why a common standard method for quantifying direct effects (i.e. stratifying on the intermediate variable) may be flawed. Estimating direct effects without bias requires that two assumptions hold, namely the absence of unmeasured confounding for (1) exposure and outcome, and (2) the intermediate variable and outcome. Recommendations include collecting and incorporating potential confounders for the causal effect of the mediator on the outcome, as well as the causal effect of the exposure on the outcome, and clearly stating the additional assumption that there is no unmeasured confounding for the causal effect of the mediator on the outcome.
Keywords Causal inference, direct effect, intermediate variable, risk ratio
Accepted 11 July 2001
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