Time-related Biases Including Immortality Bias
We were recently asked about the term “immortality bias.” The easiest way to explain immortality bias is to start with an example. Imagine a study of hospitalized COPD patients undertaken to assess the impact of drug A, an inhaled corticosteroid preparation, on survival. In our first example, people are randomized to receive a prescription to drug A post-discharge or not to receive a prescription. If someone in group A dies prior to filling their prescription, they should be analyzed as randomized and, therefore, they should be counted as a death in the drug A group even though they were never actually exposed to drug A.
Let’s imagine that drug A confers no survival advantage and that mortality for this population is 10 percent. In a study population of 1,000 patients in each group, we would expect 100 deaths in each group. Let us say that 10 people in the drug A group died before they could receive their medication. If we did not analyze the unexposed people who died in group A as randomized, that would be 90 drug A deaths as compared to 100 comparison group deaths—making it falsely appear that drug A resulted in a survival advantage.
If drug A actually works, the time that patients are not exposed to the drug works a little against the intervention (oh, yes, and do people actually take their drug?), but as bias tends to favor the intervention, this probably evens up the playing field a bit—there is a reason why we talk about “closeness to truth” and “estimates of effect.”
“Immortality bias” is a risk in studies when there is a time period (the “immortal” or the “immune” time when the outcome is other than survival) in which patients in one group cannot experience an event. Setting aside the myriad other biases that can plague observational studies, such as the potential for confounding through choice of treatment, to illustrate this, let us compare our randomized controlled trial (RCT) that we just described to a retrospective cohort study to study the same thing. In the observational study, we have to pick a time to start observing patients, and it is no longer randomly decided how patients are grouped for analysis, so we have to make a choice about that too.
For our example, let us say we are going to start the clock on recording outcomes (death) beginning at the date of discharge. Patients are then grouped for analysis by whether or not they filled a prescription for drug A within 90 days of discharge. Because “being alive” is a requirement for picking up prescription, but not for the comparison group, the drug A group potentially receives a “survival advantage” if this bias isn’t taken into account in some way in the analysis.
In other words, by design, no deaths can occur in the drug A group prior to picking up a prescription. However, in the comparison group, death never gets an opportunity to “take a holiday” as it were. If you die before getting a prescription, you are automatically counted in the comparison group. If you live and pick up your prescription, you are automatically counted in the drug A group. So the outcome of “being alive” is a prerequisite to being in the drug A group. Therefore, all deaths of people not filling a prescription that occur prior to that 90 day window get counted in the comparison group. And so yet another example of how groups being different or being treated differently other than what is being studied can bias outcomes.
Many readers will recognize the similarity between immortality bias and lead time bias. Lead time bias occurs when earlier detection of a disease, because of screening, makes it appear that the screening has conferred a survival advantage—when, in fact, the “greater length of time survived” is really an artifact resulting from the additional time counted between disease identification and when it would have been found if no screening had taken place.
Another instance where a time-dependent bias can occur is in oncology studies when intermediate markers (e.g., tumor recurrence) are assessed at the end of follow-up segments using Kaplan-Meier methodology. Recurrence may have occurred in some subjects at the beginning of the time segment rather than at the end of a time segment.
It is always good to ask if, in the course of the study, could the passing of time have had a resulting impact on any outcomes?
Other Examples —
- Might the population under study have significantly changed during the course of the trial?
- Might the time period of the study affect study results (e.g., studying an allergy medication, but not during allergy season)?
- Could awareness of adverse events affect future reporting of adverse events?
- Could test timing or a gap in testing result in misleading outcomes (e.g., in studies comparing one test to another, might discrepancies have arisen in test results if patients’ status changed in between applying the two tests)?
All of these time-dependent biases can distort study results.