Kate Tilling, Johns Hopkins University Department of Epidemiology
Many of the factors affecting all-cause mortality are both confounders
each other, and intermediate variables on the causal pathway. For example,
smokers may have higher blood pressure, which may in turn increase the
chance of death. However, those with high blood pressure may have increased
pressure to quit smoking, which may in turn reduce the chances of death.
Thus high blood pressure may be a confounder for the effect of smoking, and
also an intermediate variable on the causal pathway of smoking on
mortality. Longitudinal data are needed to assess such relationships.
However, conventional methods (such as survival models with time-varying
covariates) may be biased where this form of time-varying confounding
exists. A suggested approach is the g-estimation of causal effects (1,2).
Here I describe this approach, using longitudinal data on blood pressure,
cholesterol and smoking (from the ARIC study). The resulting estimates of
the effect of smoking cessation on mortality are compared to those obtained
ignoring the time-varying confounders.
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