Article in Biometrics, 2017. 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
0.7field-weighted citation impact, top 27% of its field
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, 10 citations in OpenAlex.
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 at 4 institutions in 2 countries.
Yun-Hee ChoiDepartment of Epidemiology and Biostatistics, Western University, London, Canada.
Laurent BriollaisLunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada.
Aung K WinMelbourne School of Population and Glogal Health, University of Melbourne, Melbourne, Australia.
John HopperMelbourne School of Population and Glogal Health, University of Melbourne, Melbourne, Australia.
Dan BuchananMelbourne School of Population and Glogal Health, University of Melbourne, Melbourne, Australia.
Mark JenkinsMelbourne School of Population and Glogal Health, University of Melbourne, Melbourne, Australia.
Lajmi Lakhal-ChaiebDepartment of Mathematics and Statistics, Laval University, Quebec, Canada.
University of Melbourne · AULunenfeld-Tanenbaum Research Institute · CAUniversité Laval · CAWestern University · CA
Funding
USC Consortium:Colorectal Cancer Family RegistryU01CA074799 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HAILE, ROBERT WILLIAM · 1997 to 2008
$20.4M
SEATTLE FAMILIAL COLORECTAL CANCER REGISTRYU01CA074794 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI NEWCOMB, POLLY A · 1997 to 2007
$14.1M
ONTARIO REGISTRY FOR STUDIES OF FAMILIAL COLON CANCERU01CA074783 · NCI · CANCER CARE ONTARIO · PI GALLINGER, STEVEN · 1997 to 2008
$12.5M
Colon Cancer Family Registry CohortUM1CA167551 · NCI · STANFORD UNIVERSITY · PI HAILE, ROBERT WILLIAM, JENKINS, MARK A · 2013 to 2017
$11.3M
FAMILIAL COLORECTAL NEOPLASIA COLLABORATIVE GROUPU01CA074800 · NCI · MAYO CLINIC ROCHESTER · PI LINDOR, NORALANE MOREY · 1997 to 2007
$9.1M
The Colon Cancer Family Registry: USC ConsortiumU24CA074799 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HAILE, ROBERT WILLIAM · 2008 to 2011
$7.6M
The Colon Cancer Family Registry: SeattleU24CA074794 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI NEWCOMB, POLLY A · 2008 to 2011
$7.0M
AUSTRALASIAN COLORECTAL CANCER FAMILY REGISTRYU01CA097735 · NCI · UNIVERSITY OF MELBOURNE · PI HOPPER, JOHN L · 2002 to 2007
$6.9M
The Colon Cancer Family Registry: AustralasiaU24CA097735 · NCI · UNIVERSITY OF MELBOURNE · PI HOPPER, JOHN L · 2008 to 2011
$6.2M
HAWAII FAMILY REGISTRY OF COLON CANCERU01CA074806 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 1997 to 2008
$6.1M
The Familial Colorectal Neoplasia Collaborative GroupU24CA074800 · NCI · MAYO CLINIC ROCHESTER · PI LINDOR, NORALANE MOREY, THIBODEAU, STEPHEN NORMAN · 2008 to 2011
$5.3M
The Colon Cancer Family Registry: OntarioU24CA074783 · NCI · CANCER CARE ONTARIO · PI GALLINGER, STEVEN · 2008 to 2011
In this article, we propose an association model to estimate the penetrance (risk) of successive cancers in the presence of competing risks. The association between the successive events is modeled via a copula and a proportional hazards model is specified for each competing event. This work is motivated by the analysis of successive cancers for people with Lynch Syndrome in the presence of competing risks. The proposed inference procedure is adapted to handle missing genetic covariates and selection bias, induced by the data collection protocol of the data at hand. The performance of the proposed estimation procedure is evaluated by simulations and its use is illustrated with data from the Colon Cancer Family Registry (Colon CFR).
Indexed as
Data Interpretation, StatisticalProportional Hazards ModelsAnalysis of VarianceBiasColonic NeoplasmsColorectal Neoplasms, Hereditary NonpolyposisComputer SimulationGeneticsHumansRegistriesRiskAscertainment correctionMissing covariatesPenetrance functionSuccessive competing risks
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.
Modeling of successive cancer risks in Lynch syndrome families in the presence of competing risks using copulas. · full record | OpenQuestion