Commentary: Estimating direct and indirect effects—fallible in theory, but in the real world?

A classic finding of the Whitehall Study was that only a third of the association of occupational grade (a socioeconomic ranking of occupations within the British civil service) with coronary heart disease mortality was ‘explained’ after adjusting for known cardiovascular risk factors.1 In current terminology, a third of the association of socioeconomic position with coronary heart disease mortality was estimated as ‘indirect’ via known risk factors, and two-thirds (that unexplained) was estimated as ‘direct’. This direct effect, it is assumed (e.g. ref. 2), represents the mediating or indirect effects of other factors (e.g. unmeasured and/or unknown dietary and lifestyle behaviours, psychosocial factors). For example, subsequent research on the Whitehall Study has suggested that workplace characteristics of control and demand explained much of the remaining two-thirds3— although this analysis has been criticized for conflating measures of socioeconomic position.4,5 The point here, though, is that this original finding from the Whitehall Study is just one example of a widespread (almost universal) practice within epidemiology that aims to describe and quantify causal pathways by controlling for possible mediating variables using standard epidemiological methods. Enter Cole and Hernán6 who, in this edition of the International Journal of Epidemiology, build on prior work of Robins and Greenland (1992)7 and Poole and Kaufman (2000)8 to demonstrate that such ‘standard’ epidemiological practice may be misleading. In brief, using counterfactual models and causal graphs they demonstrate that if an unknown variable (e.g. genotype) confounds the association of the mediating variable with the outcome, then stratifying the exposure-disease association by the mediating variable may not accurately partition the total effect into its direct and indirect components. Previous methodological work demonstrating this fallibility has used completely hypothetical examples. The Cole and Hernán example starts with actual data on randomized aspirin (exposure) and subsequent myocardial infarction (MI; outcome). However, the distribution of the potential mediating variable (platelet aggregation) and the genotype confounder remain constructed. Their data distribution is consistent with a causal and protective effect of aspirin on MI (relative risk of 0.6) being entirely due to platelet aggregation, yet when they stratify the aspirin-MI association by platelet aggregation the relative risk is unchanged. According to standard epidemiological expectation the stratified relative risk should have been 1.0. What happened? To help understand Cole and Hernán’s example, I have rearranged the data to determine RRUM|E—the relative risk of the confounder → high platelet aggregation association, stratified by the randomized aspirin exposure (Table 1). It is striking that 95% of those people ‘exposed’ to the confounder U within the unexposed (E = 0; no aspirin on a randomized intervention) had high platelet aggregation compared to 50% among the exposed (E = 1). The difference in the percentages among those not ‘exposed’ to the confounder U is also striking: 50% compared to 5%. Thus, the confounder U is strongly associated with the intermediary variable M. There is also a strong association of the confounder U with D (Table 2). The crude RRUD is 0.23, and stratified by the exposure is either 0.20 or 0.28. But what is more, the presence of the confounder (U = 1) is now protective against disease, despite also being a cause of high platelet aggregation (M = 1) which is positively associated with disease. To summarize, the Cole and Hernán example is equivalent to saying that: