Causal Inference
Illustrating the structures of bias from immortal time using directed acyclic graphs Guoyi Yang* Guoyi Yang Stephen Burgess C Mary Schooling
Background: Immortal time is a period of follow-up during which death or the study outcome cannot occur by design. Bias from immortal time has been increasingly recognized in epidemiologic studies. However, the fundamental causes and structures of bias from immortal time have not been explained systematically using a structural approach.
Methods: We use an example “Do Nobel Prize winners live longer than less recognized scientists?” for illustration. We illustrate how immortal time arises and present the structures of bias from immortal time using time-varying directed acyclic graphs (DAGs). We further explore the structures of bias with the exclusion of immortal time and with the presence of competing risks. We discuss how these structures are shared by different study designs in pharmacoepidemiology and provide solutions, where possible, to address the bias.
Results: We illustrate that immortal time arises from using postbaseline information to define exposure or eligibility. We use time-varying DAGs to explain the structures of bias from immortal time are confounding by survival until exposure allocation or selection bias from selecting on survival until eligibility. We explain that excluding immortal time from the follow-up does not fully address this confounding or selection bias, and that the presence of competing risks can worsen the bias. Bias from immortal time may be avoided by aligning time zero, exposure allocation and eligibility, and by excluding individuals with prior exposure.
Conclusions: Understanding bias from immortal time in terms of confounding or selection bias helps researchers identify and thereby avoid or ameliorate this bias.
