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immortal_time_bias
A bias in observational studies where a period of follow-up during which the outcome cannot occur (because the exposure has not yet happened) is misclassified as exposed person-time. This artificially inflates the exposed group's survival time and makes the exposure appear protective.
A study of Oscar winners' longevity counts the years before winning the Oscar as 'winner' person-time, during which the person was 'immortal' (they had to survive to win). This artificially increases the winners' apparent life expectancy.
A study claims that patients who complete a full 12-week cardiac rehabilitation program have 40% lower mortality than those who don't. But patients who died in the first 8 weeks were automatically classified as 'non-completers' — they couldn't have completed the program because they died. The completers were immortal during those early weeks by definition, inflating their apparent survival advantage.
Researchers find that employees who receive a promotion live longer on average than those who don't. However, the years worked before receiving the promotion — during which death would have prevented the promotion from ever occurring — are counted as 'promoted' person-time, making promoted employees appear healthier than they actually are relative to non-promoted peers.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Is a cohort study comparing exposed and unexposed groups over time?
Type: binaryIs there a period during which exposed individuals could not have experienced the outcome by definition?
Type: binaryIs this 'immortal' time being counted in the exposed group's person-time, artificially lowering their event rate?
Type: binaryA bias in observational studies where a period of follow-up during which the outcome cannot occur (because the exposure has not yet happened) is misclassified as exposed person-time. This artificially inflates the exposed group's survival time and makes the exposure appear protective.
The bias is subtle because the immortal time genuinely occurs and feels like it belongs to the exposed group. The logical error of requiring survival to receive the exposure is easy to overlook.
Properly classify person-time by exposure status at each time point. Use time-dependent exposure analysis or landmark analysis to avoid immortal time bias.
Pharmacoepidemiology, studies of lifestyle interventions, and any observational study where exposure occurs after study entry.
Prevalence studies miss fatal or short-duration cases, distorting disease-exposure associations.
Temporal trends or changes in practice during a study period distort comparisons.
Using information that was not available at the point in time being analyzed.
Use these tools to detect, analyze, or train this aspect.