Evidence map›Paper›PMID 38504141›Full record

ArticleGenetic epidemiology2024

Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies.

John L Hopper, Shuai Li, Robert J MacInnis, James G Dowty, Tuong L Nguyen, Minh Bui, Gillian S Dite, Vivienne F C Esser, Zhoufeng Ye, Enes Makalic and 21 more

Abstract read
In one paragraph

Article in Genetic epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

31 authors.

John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Shuai LiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-8696-8594
Robert J MacInnisCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
James G DowtyCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-6597-8363
Minh BuiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Gillian S DiteCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Vivienne F C EsserCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Zhoufeng YeCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-0884-4246
Enes MakalicCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Daniel F SchmidtDepartment of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia.
Benjamin GoudeyARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-2318-985X
Karen AlpenCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Miroslaw KapuscinskiCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Aung Ko WinCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Pierre-Antoine DuguéCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Roger L MilneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Harindra JayasekaraCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Jennifer D BrooksDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-7574-4256
Sue MaltaCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Lucas Calais-FerreiraCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0001-6153-8080
Alexander C CampbellCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Jesse T YoungCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Tu Nguyen-DumontPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Joohon SungDepartment of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea.
Graham G GilesCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.
Daniel BuchananDepartment of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia.
Ingrid WinshipDepartment of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
Mary Beth TerryDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia.

Funding

National Health and Medical Research Council GNT1178027National Health and Medical Research Council GNT2017373National Research Foundation of Korea (NRF) 2020R1A2C2101041National Research Foundation of Korea (NRF) 2023R1A2C2002925Suicide Prevention Australia
6 · The paper itself

Abstract

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Indexed as

Breast NeoplasmsTwin Studies as TopicAdultAge FactorsColorectal NeoplasmsFamilyFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansIncidenceIntestinal NeoplasmsMaleMiddle AgedModels, StatisticalRegistriesbreast cancercausationcolorectal cancerDEPTHfamilial confoundingfamily dataICE CRISTALICE FALCONstatistical methodstwin studiesVALID

Identifiers

PMID38504141
PMCPMC11589006

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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.