ArticleAmerican journal of epidemiology2019
Point: Immortal Time Bias-What Are the Determinants of Its Magnitude?
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identifying, handling and impact of immortal time bias on addressing treatment effects in observational studies using routinely collected data.BMC medical research methodology · 2025Pooled it
- Oseltamivir Reduces 30-Day Mortality in Older Adults With Influenza: A Pooled Analysis From the 2012-2019 Serious Outcomes Surveillance Network of the Canadian Immunization Research Network.Open forum infectious diseases · 2025Trial
- Evaluation of Time-related Bias With Non-user Control.Journal of epidemiology · 2026Article
- Bariatric metabolic surgery and cancer risk: Target trial emulation using iterative time distribution matching.Biomolecules & biomedicine · 2025Article
- Considerations for selecting and implementing comorbidity indices when using secondary data sources: a guide for health researchers.International journal of population data science · 2025Review
- Medication Exposure and Mortality in Patients With Schizophrenia.JAMA network open · 2024Article
- Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential.Frontiers in medicine · 2024Article
- Comparing broad and narrow phenotype algorithms: differences in performance characteristics and immortal time incurred.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2023Article
- Immortal time bias for life-long conditions in retrospective observational studies using electronic health records.BMC medical research methodology · 2022Article
- Pharmacoepidemiology for nephrologists (part 2): potential biases and how to overcome them.Clinical kidney journal · 2021Review
- Conditional survival in patients with esophageal or gastroesophageal junction cancer after receiving various treatment modalities.Cancer medicine · 2021Article
- Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time.American journal of epidemiology · 2019Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In cohort studies, "immortal time" bias refers to a portion of time during which events cannot occur for a particular group of participants. Typically, immortal time bias occurs when: 1) Exposure can be initiated after follow-up of cohort members has begun; and 2) analytically, the preexposure experience is combined with that which takes place following exposure, rather than (correctly) as part of the experience of nonexposed individuals. Using the example of a cohort study of mortality in relation to receipt of cataract surgery, we sought to describe those study design and population characteristics that influence the magnitude of immortal time bias, so as to aid readers in gauging its impact on published research findings. These characteristics include the mean interval between cohort entry and when exposure criteria are met, the proportion of exposed study participants, and the length of study follow-up.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.