What is immortal time bias?

Immortal time bias

Immortal time bias is a type of bias that can occur in observational research when the study design allows for a period of time during which the outcome of interest cannot occur, often referred to as “immortal time”.

Simply put, immortal time bias occurs when information from a future event is incorporated into the analysis already at baseline (that is, start of follow-up), making all individuals who experience that future event immortal up until that point in time.

For example, consider a study examining the effect of a new drug on mortality in patients with a certain disease. If the new drug is administered at some time point after diagnosis, for example 6 months later, patients who receive the drug will be immortal during those first 6 months. This leads to an overestimation of the effect of the new drug on mortality, as those who did not receive the drug will include those who died before having a chance to get treated.

Immortal time bias will not occur in randomised controlled trials, as they are performed “in real time” – making it impossible to use future information. However, it can occur in many different types of observational studies, including cohort studies, case-control studies, and cross-sectional studies. In these studies, it is avoided by carefully defining the study population and the time period over which outcomes are assessed.


State-of-the-art statistical models for modern HTA

At @RedDoorAnalytics, we develop methodology and software for efficient modelling of biomarkers, measured repeatedly over time, jointly with survival outcomes, which are being increasingly used in cancer settings. We have also developed methods and software for general non-Markov multi-state survival analysis, allowing for the development of more plausible natural history models, where patient history can […]
Learn more


Multilevel (hierarchical) survival models: Estimation, prediction, interpretation

Hierarchical time-to-event data is common across various research domains. In the medical field, for instance, patients are often nested within hospitals and regions, while in education, students are nested within schools. In these settings, the outcome is typically measured at the individual level, with covariates recorded at any level of the hierarchy. This hierarchical structure […]
Learn more

Statistical Primers

What are competing risks?

Competing risks In survival analysis, competing risks refer to the situation when an individual is at risk of experiencing an event that precludes the event under study to occur. Competing risks commonly occur in studies of cause-specific mortality, as all other causes of death than the one under study might happen before the individuals “have […]
Learn more