TY - JOUR
T1 - Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies
T2 - Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci
AU - , on behalf of the Ovarian Cancer Association Consortium
AU - Clyde, Merlise A.
AU - Palmieri Weber, Rachel
AU - Iversen, Edwin S.
AU - Poole, Elizabeth M.
AU - Doherty, Jennifer A.
AU - Goodman, Marc T.
AU - Ness, Roberta B.
AU - Risch, Harvey A.
AU - Rossing, Mary Anne
AU - Terry, Kathryn L.
AU - Wentzensen, Nicolas
AU - Whittemore, Alice S.
AU - Anton-Culver, Hoda
AU - Bandera, Elisa V.
AU - Berchuck, Andrew
AU - Carney, Michael E.
AU - Cramer, Daniel W.
AU - Cunningham, Julie M.
AU - Cushing-Haugen, Kara L.
AU - Edwards, Robert P.
AU - Fridley, Brooke L.
AU - Goode, Ellen L.
AU - Lurie, Galina
AU - McGuire, Valerie
AU - Modugno, Francesmary
AU - Moysich, Kirsten B.
AU - Olson, Sara H.
AU - Pearce, Celeste Leigh
AU - Pike, Malcolm C.
AU - Rothstein, Joseph H.
AU - Sellers, Thomas A.
AU - Sieh, Weiva
AU - Stram, Daniel
AU - Thompson, Pamela J.
AU - Vierkant, Robert A.
AU - Wicklund, Kristine G.
AU - Wu, Anna H.
AU - Ziogas, Argyrios
AU - Tworoger, Shelley S.
AU - Schildkraut, Joellen M.
N1 - Publisher Copyright:
© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].
PY - 2016/10/15
Y1 - 2016/10/15
N2 - 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.
AB - 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.
KW - genetic risk polymorphisms
KW - model evaluation
KW - ovarian cancer
KW - risk model
UR - http://www.scopus.com/inward/record.url?scp=85047583412&partnerID=8YFLogxK
M3 - Article
C2 - 27698005
AN - SCOPUS:85047583412
SN - 0002-9262
VL - 184
SP - 579
EP - 589
JO - American Journal of Epidemiology
JF - American Journal of Epidemiology
IS - 8
ER -