Evidence map›Paper›PMID 39777475›Full record

ArticleInternational journal of epidemiology2024

Illustrating the structures of bias from immortal time using directed acyclic graphs.

Guoyi Yang, Stephen Burgess, Catherine Mary Schooling

Abstract read
In one paragraph

Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The price of fame? Mortality risk among famous singers.Journal of epidemiology and community health · 2026
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Guoyi YangSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.ORCID 0000-0002-4387-386X
Stephen BurgessMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0001-5365-8760
Catherine Mary SchoolingSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.ORCID 0000-0001-9933-5887

Funding

Medical Research Council MC_UU_00002/7Wellcome Trust 225790Wellcome Trust 225790/Z/22/Z
6 · The paper itself

Abstract

backgroundImmortal time is a period of follow-up during which death or the study outcome cannot occur by design. Bias from immortal time has been increasingly recognized in epidemiological studies. However, the fundamental causes and structures of bias from immortal time have not been explained systematically.

methodsWe use an example 'Does winning a Nobel Prize prolong lifespan?' for illustration. We illustrate how immortal time arises and present structures of bias from immortal time using directed acyclic graphs that specify time-varying variables. We further explore the structures of bias with the exclusion of immortal time and with the presence of competing risks. We discuss how these structures are shared by different study designs in pharmacoepidemiology and provide solutions, where possible, to address the bias.

resultsThe fundamental cause of immortal time is misalignment of exposure allocation and eligibility. Specifically, immortal time arises from using post-eligibility information to define exposure or using post-exposure information to define eligibility. The structures of bias from immortal time are confounding by survival until exposure allocation or selection bias from selecting on survival until eligibility. Excluding immortal time from follow-up does not fully address this confounding or selection bias, and the presence of competing risks can worsen the bias. Bias from immortal time may be avoided by aligning baseline, exposure allocation and eligibility, and by excluding individuals with prior exposure.

conclusionsUnderstanding bias from immortal time in terms of confounding or selection bias helps researchers identify and thereby avoid or ameliorate this bias.

Indexed as

BiasConfounding Factors, EpidemiologicHumansPharmacoepidemiologySelection BiasTime FactorsBiasdirected acyclic graphsepidemiologyimmortal time

Identifiers

PMID39777475
PMCPMC11706530

What Socratic holds

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Registered trials

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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 Socratic graph.