Statistics, immortal time

Immortal time in observational studies can bias the results in favour of the treatment group, but it is not difficult to identify and avoid.
Well designed observational studies have made important contributions to our understanding of the risks and benefits of drug treatment. Such studies are often the first to identify or confirm important adverse health events associated with drugs, as seen recently with the cardiac effects of ergot derived dopamine agonists’ and cyclo-oxygenase 2 inhibitors. Observational studies can also assess aspects of drug safety, such as the time varying nature of risk, which cannot be readily appraised using an experimental design.”
Cohort studies are often preferred to case-control studies because they are less susceptible to certain biases. However, the inappropriate accounting of follow-up time and treatment status in the design and analysis of such studies can introduce immortal time bias.
What is immortal time bias?
Immortal time refers to a period of follow-up during which, by design, death or the study outcome cannot occur. In pharmacoepidemiology studies, immortal time typically arises when the determination of an individual’s treatment status involves a delay or wait period during which follow¬ up time is accrued-for example, waiting for a prescrip¬tion to be dispensed after discharge from hospital when the discharge date represents the start of follow-up. This wait period is considered immortal because individuals who end up in the treated or exposed group have to survive (be alive and event free) until the treatment definition is fulfilled. If they have an event before taking up treatment they are in the untreated or unexposed group. Bias is introduced when this period of “immortality” is either misclassified with regards to treatment status or excluded from the analysis. Immortal time bias is particularly problematic because it necessarily biases the results in favour of the treatment under study by conferring a spurious survival advantage to the treated group.
Immortal time bias is increasingly common in cohort studies of drug effects.

Criteria for identifying immortal time bias in cohort studies

• Was treatment status determined after the start of follow-up or defined using follow-up time?
• Was the start of follow-up different for the treated and untreated (or comparator) group’ relative to the date of diagnosis? ,
• Were the treatment groups identified hierarchically (one group before the other)?
• Were subjects excluded on the basis of treatment identified during follow-up?
• Was a time fixed analysis used?

Levesque et al 2010 problems of immortal time bias in cohort studies: example using stains for preventing progression of diabetes , BMJ vol 340 pp 907-911

Martin Eastwood
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