Interpreting confounder and modifier coefficients

13 Feb, 2023 at 10:37 | Posted in Statistics & Econometrics | Comments Off on Interpreting confounder and modifier coefficients

The problem with a table presenting multiple estimated effect measures from the same model (“Table 2”) is that it encourages the reader to interpret all these estimates in the same way, typically as total-effect estimates. As illustrated above, the interpretation of a confounder effect estimate may be different than for the exposure effect estimate. Of course, it is possible that some secondary reported associations are unbiased for total effects and that others are unbiased for direct effects; nonetheless, the assumption that all estimates reported in Table 2 are for total effects is not warranted. Thus, we recommend that a presentation of secondary effect estimates would best specify the type of effect being estimated.

As in all causal modeling, the interpretations described above should raise questions about the ethics and feasibility of the interventions implicit in the effect definitions. In the example, definition of the direct effect of age requires holding a person’s smoking level and HIV status constant as they age, which would be unethical for smokers (among whom reduction should be encouraged) and infeasible even if desirable for those HIV negative (because some will maintain unsafe practices). More generally, definitions of direct and indirect effects involve combined interventions on both the exposure and mediators; some combinations may resemble nothing anyone would consider in reality, thus violating positivity constraints.

In sum, presenting estimates of effect measures for secondary risk factors (confounders and modifiers of the exposure effect measure) obtained from the same model as that used to estimate the primary exposure effects can lead readers astray in a number of ways. Extra thought and description will be needed when interpreting such secondary estimates.

Daniel Westreich & Sander Greenland