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Immortal Time Bias in Pharmacoepidemiology

2007· review· en· 1,669 citations· W2111714917 on OpenAlex· 10.1093/aje/kwm324

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Opus teacher head0.219
GPT teacher head0.491
Teacher spread
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score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Immortal time is a span of cohort follow-up during which, because of exposure definition, the outcome under study could not occur. Bias from immortal time was first identified in the 1970s in epidemiology in the context of cohort studies of the survival benefit of heart transplantation. It recently resurfaced in pharmaco-epidemiology, with several observational studies reporting that various medications can be extremely effective at reducing morbidity and mortality. These studies, while using different cohort designs, all involved some form of immortal time and the corresponding bias. In this paper, the author describes various cohort study designs leading to this bias, quantifies its magnitude under different survival distributions, and illustrates it by using data from a cohort of lung cancer patients. The author shows that for time-based, event-based, and exposure-based cohort definitions, the bias in the rate ratio resulting from misclassified or excluded immortal time increases proportionately to the duration of immortal time. The bias is more pronounced with a decreasing hazard function for the outcome event, as illustrated with the Weibull distribution compared with a constant hazard from the exponential distribution. In conclusion, observational studies of drug benefit in which computerized databases are used must be designed and analyzed properly to avoid immortal time bias.

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The record

Venue
American Journal of Epidemiology
Topic
Liver Disease Diagnosis and Treatment
Field
Medicine
Canadian institutions
McGill University
Funders
Canadian Institutes of Health Research
Keywords
MedicineHazard ratioObservational studyCohortContext (archaeology)EpidemiologyCohort studyProportional hazards modelPharmacoepidemiologySurvival analysisStatisticsInternal medicineConfidence intervalPharmacologyMathematics
Has abstract in OpenAlex
yes