ArticleAmerican journal of epidemiology2026
Evaluating statistical models for overdispersed multiomics data: a multiplex immunofluorescence case study.
Claire E Thomas, Evertine Wesselink, Yasutoshi Takashima, Jeroen R Huyghe, Daniel D Buchanan, Robert C Grant, Andressa Dias Costa, Tomotaka Ugai, Shuji Ogino, Jonathan A Nowak and 3 more
Abstract read
In one paragraphArticle in American journal of epidemiology, 2026. 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
13 authors.
Claire E ThomasPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.ORCID 0000-0001-9515-3277 Evertine WesselinkDivision of Molecular Pathology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Yasutoshi TakashimaDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States.
Jeroen R HuyghePublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.
Daniel D BuchananColorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia.
Robert C GrantDivision of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Andressa Dias CostaDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, United States.
Tomotaka UgaiProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.ORCID 0000-0003-0182-5269 Shuji OginoProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-3909-2323 Jonathan A NowakProgram in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
Ulrike PetersPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.ORCID 0000-0001-5666-9318 Amanda I PhippsPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.
Funding
Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4MStatistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, TAMIMI, RULLA M · 2000 to 2019
$77.8MValidity of Diet and Activity Measures in WomenP01CA055075 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI FUCHS, CHARLES S · 1991 to 2009
$41.8MLong Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3MMolecular pathological epidemiology of colorectal cancerU01CA137088 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PETERS, ULRIKE · 2009 to 2018
$21.8MData sharing: the Colon Cancer Family Registry CohortU01CA167551 · NCI · UNIVERSITY OF MELBOURNE · PI Daniel David BUCHANAN, Steven Gallinger · 2018 to 2026
$16.8MONTARIO REGISTRY FOR STUDIES OF FAMILIAL COLON CANCERU01CA074783 · NCI · CANCER CARE ONTARIO · PI GALLINGER, STEVEN · 1997 to 2008
$12.5MDetection of Colorectal Cancer Susceptibility Loci Using Genome-Wide SequencingU01CA164930 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PETERS, ULRIKE · 2012 to 2015
$12.4MCancer Epidemiology Cohort in Male Health ProfessionalsUM1CA167552 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI WILLETT, WALTER C. · 2012 to 2016
$11.5MGenome-Wide Association Study of Nonsynonymous SNPs in Colon CancerR01CA059045 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PETERS, ULRIKE · 1994 to 2011
$11.5MUsing Functional Genomics to Inform Gene Environment Interactions for Colorectal CancerR01CA201407 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CASEY, GRAHAM, GAUDERMAN, WILLIAM JAMES · 2016 to 2020
$9.6MSpatial Immunopathological Epidemiology of Colorectal Adenoma-Carcinoma SpectrumR01CA248857 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Jonathan Andrew Nowak, Shuji Ogino · 2020 to 2026
$6.6MAmerican Cancer Society Clinical Research Professor CRP-24-1185864-01-PROFCancer Research UK Grand Challenge C10674/A27140National Health and Medical Research Council of Australia Investigator GNT1194896National Health and Medical Research Council of Australia Investigator S10OD028685NCI, NIH U01 CA167551NCI/NIH U01/U24 CA074783NCI NIH HHS P01 CA055075NCI NIH HHS P01 CA087969NCI NIH HHS P20 CA252733NCI NIH HHS P30 CA015704NCI NIH HHS R01 CA059045NCI NIH HHS R01 CA151993NCI NIH HHS R01 CA189532NCI NIH HHS R01 CA201407NCI NIH HHS R01 CA206279NCI NIH HHS R01 CA244588NCI NIH HHS R01 CA248857NCI NIH HHS R01 CA297681NCI NIH HHS R35 CA197735NCI NIH HHS U01 CA074783NCI NIH HHS U01 CA137088NCI NIH HHS U01 CA164930NCI NIH HHS U01 CA167551NCI NIH HHS U24 CA074783NCI NIH HHS UM1 CA167552NCI NIH HHS UM1 CA186107NCI, NIH, US Department of Health and Human Services P20 CA252733NCI, NIH, US Department of Health and Human Services R01 CA059045NCI, NIH, US Department of Health and Human Services R01 CA 189532NCI, NIH, US Department of Health and Human Services R01 CA201407NCI, NIH, US Department of Health and Human Services R01 CA206279NCI, NIH, US Department of Health and Human Services R01 CA244588NCI, NIH, US Department of Health and Human Services R01 CA248857NCI, NIH, US Department of Health and Human Services R01 CA 297681NCI, NIH, US Department of Health and Human Services U01 CA137088NCI, NIH, US Department of Health and Human Services U01 CA164930NIHNIH HHS S10 OD028685NIH/NCI Cancer Center Support P30 CA015704OFCCR/ARCTIC GL201-043Ontario Research Fund (to B.W.Z.) 112746US NIH R01 CA248857US NIH R35 CA197735US NIH R50 CA247122
6 · The paper itselfAbstract
Multiomic data analysis poses statistical challenges. We evaluated statistical models for our multiplex immunofluorescence study of T cell subset densities in colorectal cancer. Using 1235 cases, we compared 7 models-ordinal logistic regression, Poisson, quasi-Poisson, quadratic negative binomial (NB), linear NB, zero-inflated NB, and hurdle NB models-assessing associations with a strong (microsatellite instability, MSI) and a weak (calcium intake) exposure. Simulation studies assessed type I error and power. Effect estimates were generally consistent for the strong exposure (MSI) but varied for the weaker exposure (calcium). Simulations revealed inflated false-positive rates for the Poisson and NB-based models, including quadratic NB, zero-inflated, and hurdle, but not for ordinal logistic regression or the linear NB model. The quasi-Poisson model showed modest inflation of low P-values, but the overall P-value distribution remained approximately uniform under the null. Ordinal logistic, linear NB, and quasi-Poisson models achieved the best or near-best power across a range of zero proportions, dispersion levels, and distributions. The ordinal logistic, linear NB, and quasi-Poisson models are useful and robust options for epidemiologic analyses of overdispersed, right-skewed multiomic data with a nontrivial proportion of zero counts.
Indexed as
Colorectal NeoplasmsModels, StatisticalMultiomicsComputer SimulationHumansLogistic ModelsPoisson Distributioncount data modelingnegative binomialordinal logistic regressionoverdispersionsimulationzero inflated data
Identifiers
PMID42294596
PMCPMC13437073
What OpenQuestion holds
Textmetadata
LicenceCC BY
Read underepoch 390