Fallibility in estimating direct effects.
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.
Cole
Stephen R
SR
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. scole@jhsph.edu
Hernán
Miguel A
MA
D001241:Aspirin / Q000627:therapeutic use; D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D009203:Myocardial Infarction / Q000188:drug therapy / Q000453:epidemiology*; D016017:Odds Ratio; D010975:Platelet Aggregation Inhibitors / Q000627:therapeutic use
D010975:Platelet Aggregation Inhibitors; D001241:Aspirin