Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

dc.contributor.author

Clyde, Merlise A

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Palmieri Weber, Rachel

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Iversen, Edwin S

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Poole, Elizabeth M

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Doherty, Jennifer A

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Goodman, Marc T

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Ness, Roberta B

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Risch, Harvey A

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Rossing, Mary Anne

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Terry, Kathryn L

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Wentzensen, Nicolas

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Whittemore, Alice S

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Anton-Culver, Hoda

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Bandera, Elisa V

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Berchuck, Andrew

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Carney, Michael E

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Cramer, Daniel W

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Cunningham, Julie M

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Cushing-Haugen, Kara L

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Edwards, Robert P

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Fridley, Brooke L

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Goode, Ellen L

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Lurie, Galina

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McGuire, Valerie

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Modugno, Francesmary

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Moysich, Kirsten B

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Olson, Sara H

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Pearce, Celeste Leigh

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Pike, Malcolm C

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Rothstein, Joseph H

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Sellers, Thomas A

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Sieh, Weiva

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Stram, Daniel

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Thompson, Pamela J

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Vierkant, Robert A

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Wicklund, Kristine G

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Wu, Anna H

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Ziogas, Argyrios

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Tworoger, Shelley S

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Schildkraut, Joellen M

dc.coverage.spatial

United States

dc.date.accessioned

2016-10-06T18:30:37Z

dc.date.issued

2016-10-15

dc.description.abstract

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

dc.identifier

http://www.ncbi.nlm.nih.gov/pubmed/27698005

dc.identifier

kww091

dc.identifier.eissn

1476-6256

dc.identifier.uri

https://hdl.handle.net/10161/12934

dc.language

eng

dc.publisher

Oxford University Press (OUP)

dc.relation.ispartof

Am J Epidemiol

dc.relation.isversionof

10.1093/aje/kww091

dc.subject

genetic risk polymorphisms

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model evaluation

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ovarian cancer

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risk model

dc.title

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

dc.type

Journal article

duke.contributor.orcid

Clyde, Merlise A|0000-0002-3595-1872

duke.contributor.orcid

Iversen, Edwin S|0000-0002-0066-2763

pubs.author-url

http://www.ncbi.nlm.nih.gov/pubmed/27698005

pubs.begin-page

579

pubs.end-page

589

pubs.issue

8

pubs.organisational-group

Clinical Science Departments

pubs.organisational-group

Duke

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Duke Cancer Institute

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Institutes and Centers

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Obstetrics and Gynecology

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Obstetrics and Gynecology, Gynecologic Oncology

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School of Medicine

pubs.organisational-group

Statistical Science

pubs.organisational-group

Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

184

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