Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.
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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.
Published Version (Please cite this version)10.1093/aje/kww091
Publication InfoAnton-Culver, H; Bandera, EV; Berchuck, Andrew; Carney, ME; Clyde, Merlise; Cramer, DW; ... Ziogas, A (2016). Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. Am J Epidemiol, 184(8). pp. 579-589. 10.1093/aje/kww091. Retrieved from https://hdl.handle.net/10161/12934.
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James M. Ingram Professor of Gynecologic Oncology
Dr. Andrew Berchuck is Director of the Duke Division of Gynecologic Oncology and holds the James M. Ingram Distinguished Professorship. He is a practicing oncologist who is actively involved in the surgical and chemotherapy management of women with ovarian, endometrial and lower genital tract cancers. This includes minimally invasive laparoscopic surgical approaches. He also has developed a research program that focuses on the molecular-genetic alterations involved in malignant transformation of
Professor of Statistical Science
Model uncertainty and choice in prediction and variable selection problems for linear, generalized linear models and multivariate models. Bayesian Model Averaging. Prior distributions for model selection and model averaging. Wavelets and adaptive kernel non-parametric function estimation. Spatial statistics. Experimental design for nonlinear models. Applications in proteomics, bioinformatics, astro-statistics, air pollution and health effects, and environmental sciences.
Research Professor of Statistical Science
Bayesian statistical modeling with application to problems in genetic epidemiology and cancer research; models for epidemiological risk assessment, including hierarchical methods for combining related epidemiological studies; ascertainment corrections for high risk family data; analysis of high-throughput genomic data sets.
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