Evidence map›Paper›PMID 35984990›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2022

The Timing, the Treatment, the Question: Comparison of Epidemiologic Approaches to Minimize Immortal Time Bias in Real-World Data Using a Surgical Oncology Example.

Emilie D Duchesneau, Bradford E Jackson, Michael Webster-Clark, Jennifer L Lund, Katherine E Reeder-Hayes, Anna M Nápoles, Paula D Strassle

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Mycophenolate and azathioprine in fibrotic interstitial lung disease.American journal of respiratory and critical care medicine · 2026
    Observational
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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

7 authors.

Emilie D DuchesneauDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-0974-8539
Bradford E JacksonLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0003-0950-2907
Michael Webster-ClarkDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-8079-8504
Jennifer L LundDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0002-1108-0689
Katherine E Reeder-HayesLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.ORCID 0000-0001-6903-0177
Anna M NápolesDivision of Intramural Research, National Institute on Minority Health and Health Disparities, NIH, Bethesda, Maryland.ORCID 0000-0001-8838-2899
Paula D StrassleDivision of Intramural Research, National Institute on Minority Health and Health Disparities, NIH, Bethesda, Maryland.ORCID 0000-0002-1072-1560

Funding

Translating Behavioral Interventions for Health Disparity PopulationsZIAMD000014 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI CHOI, KELVIN · 2018 to 2023
$4.8M
Cancer Care Quality Training ProgramT32CA116339 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ETHAN M. BASCH, Stephanie Brooke Wheeler · 2018 to 2026
$3.0M
Intramural NIH HHS Z99 MD999999NCI NIH HHS T32 CA116339
6 · The paper itself

Abstract

backgroundStudies evaluating the effects of cancer treatments are prone to immortal time bias that, if unaddressed, can lead to treatments appearing more beneficial than they are.

methodsTo demonstrate the impact of immortal time bias, we compared results across several analytic approaches (dichotomous exposure, dichotomous exposure excluding immortal time, time-varying exposure, landmark analysis, clone-censor-weight method), using surgical resection among women with metastatic breast cancer as an example. All adult women diagnosed with incident metastatic breast cancer from 2013-2016 in the National Cancer Database were included. To quantify immortal time bias, we also conducted a simulation study where the "true" relationship between surgical resection and mortality was known.

results24,329 women (median age 61, IQR 51-71) were included, and 24% underwent surgical resection. The largest association between resection and mortality was observed when using a dichotomized exposure [HR, 0.54; 95% confidence interval (CI), 0.51-0.57], followed by dichotomous with exclusion of immortal time (HR, 0.62; 95% CI, 0.59-0.65). Results from the time-varying exposure, landmark, and clone-censor-weight method analyses were closer to the null (HR, 0.67-0.84). Results from the plasmode simulation found that the time-varying exposure, landmark, and clone-censor-weight method models all produced unbiased HRs (bias -0.003 to 0.016). Both standard dichotomous exposure (HR, 0.84; bias, -0.177) and dichotomous with exclusion of immortal time (HR, 0.93; bias, -0.074) produced meaningfully biased estimates.

conclusionsResearchers should use time-varying exposures with a treatment assessment window or the clone-censor-weight method when immortal time is present. IMPACT: Using methods that appropriately account for immortal time will improve evidence and decision-making from research using real-world data.

Indexed as

Breast NeoplasmsSurgical OncologyAdultBiasFemaleHumansMiddle AgedResearch DesignTime Factors

Identifiers

PMID35984990
PMCPMC9627261

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.