Evidence map›Paper›PMID 40409575›Full record

ArticleAnnals of epidemiology2025

Examining bias due to method of follow-up for cancer incidence in a large U.S. cohort: Self-report versus registry linkage.

Danping Liu, Martha S Linet, Paul S Albert, Cato M Milder, Annelie M Landgren, Allison Iwan, Lynn Penberthy, Bruce H Alexander, Amy Berrington de González, Cari M Kitahara

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Danping LiuBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Martha S LinetRadiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Paul S AlbertBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Cato M MilderRadiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Annelie M LandgrenSurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Allison IwanDivision of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Lynn PenberthySurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Bruce H AlexanderDivision of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Amy Berrington de GonzálezDivision of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom.
Cari M KitaharaRadiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. Electronic address: kitaharac@mail.nih.gov.

Funding

Studies of Populations Exposed to Occupational Sources of RadiationZIACP010133 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI KITAHARA, CARI · 2009 to 2025
$34.2M
Intramural NIH HHS Z99 CA999999Intramural NIH HHS ZIA CP010133
6 · The paper itself

Abstract

backgroundCancer incidence follow-up of many U.S. cohort studies relies on self-report due to the lack of a national cancer registry.

methodsIn the U.S. Radiologic Technologists (USRT) Study, cancer incidence has been captured via self-report approximately every 10 years between the mid-1980s and mid-2010s and via linkages with state cancer registries during 1999-2012. We compared associations of smoking and lung cancer (rapidly fatal) and body mass index and thyroid cancer (rarely fatal) according to method of cancer incidence follow-up: self-report versus registry-based. Simulation studies were performed to quantify bias and assess relative efficiency of cancer ascertainment methods.

resultsDuring 1999-2012, hazard ratio (HR) estimates were similar regardless of cancer follow-up method, although self-reported data yielded the widest confidence intervals (CIs). Simulation studies showed that registry data yielded the least-biased HRs. The usefulness of self-reported data depends mostly on its specificity: with perfect specificity, self-reported data led to nearly unbiased inference. However, with imperfect specificity, self-reported data resulted in 3-9 % bias and lower than nominal CI coverage.

conclusionsThese findings highlight the benefits of registry-based cancer incidence follow-up compared to self-report in contemporary U.S. cohort studies assuming high coverage by registries of the study population.

Indexed as

NeoplasmsRegistriesSelf ReportAdultAgedBiasCohort StudiesFemaleFollow-Up StudiesHumansIncidenceLung NeoplasmsMaleMiddle AgedProportional Hazards ModelsSmokingCancer registriesCohort studiesEpidemiologyLinkage

Identifiers

PMID40409575
PMCPMC12965714

What OpenQuestion 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 OpenQuestion graph.