Initial Description and Demonstration of a Family of Methods with the Potential to Approximate Confounding

Confounding is one of the most important concerns for randomized or nonrandomized intervention or exposure studies. This manuscript describes several metrics intended to provide quantitative approximations of confounding under certain conditions. Each metric quantifies differences in risk between intervention arms during time periods when the intervention (or exposure of interest) is not occurring . Because exposure is absent, these metrics have the potential to summarize the effects of other measured and unmeasured factors on outcome risk. A null association (e.g., a risk, rate, or hazard ratio of approximately 1.0 or a risk difference of 0.0) between intervention arms during nonexposure can suggest equal impacts from baseline factors affecting each study arm (i.e., an absence of confounding). However, other factors such as attrition bias, postexposure effects, or incomplete representation of the full cohort can also affect risks during nonexposure, causing the nonexposure risk to represent confounding less accurately. We propose four nonexposure metrics designed to limit these other influences on nonexposure risk, thus providing nonexposure risks that more exclusively reflect confounding. The metrics, however, vary in their potential to limit these other influences and also vary in their sensitivity to random error. We then demonstrate what we expect to be the most widely useful metric currently, the “briefly-exposed postexposure (bePE) risk metric.” We show how the bePE risk metric can inform multiple aspects of a real-world study, such as cohort derivation and interpretation of findings. Definitive validation of nonexposure risk metrics awaits further research. Nevertheless, these metrics have the potential to substantially improve intervention and exposure studies by approximating confounding under certain conditions. Their testing and validation should be a research priority.