Grant Number:	4R01CA164305-04 Interpret this number
Primary Investigator:	Chen, Jinbo
Organization:	University Of Pennsylvania
Project Title:	Statistical Methods for Cancer Absolute Risk Prediction
Fiscal Year:	2016

DESCRIPTION (provided by applicant): Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis issued by the National Cancer Institute (NCI) supports research on innovative statistical methods for developing and evaluating new and existing cancer risk prediction models. Predictive accuracy is a key factor for determining clinical applicability of cancer risk prediction models in identifying high-risk individuals for ealy prevention. With continuously emerging risk factors for cancer, there is a pressing need for timely assessment of their added values for prediction. But study designs that balance cost and statistical efficiency and accompanying statistical methods are mostly lacking. This proposal responds to this PA by developing and evaluating cost-effective two-phase stratified case-control study designs and statistical inference methods for developing and evaluating absolute risk prediction models for cancer. Our proposed work is built on a popular method that integrates case-control data and external hazard rates of cancer and mortality to predict cancer absolute risk. As a showcase example for our statistical methods and accompanying software, we explore the added value of volumetric breast density for predicting breast cancer risk.




Novel Two-Phase Sampling Designs for Studying Binary Outcomes.
Authors: Wang L. , Williams M.L. , Chen Y. , Chen J. .
Source: Biometrics, 2019-08-26 00:00:00.0; , .
EPub date: 2019-08-26 00:00:00.0.
PMID: 31449330
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Breast and ovarian cancer penetrance of BRCA1/2 mutations among Hong Kong women.
Authors: Zhang L. , Shin V.Y. , Chai X. , Zhang A. , Chan T.L. , Ma E.S. , Rebbeck T.R. , Chen J. , Kwong A. .
Source: Oncotarget, 2018-05-18 00:00:00.0; 9(38), p. 25025-25033.
EPub date: 2018-02-02 00:00:00.0.
PMID: 29861850
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Efficient unified rare variant association test by modeling the population genetic distribution in case-control studies.
Authors: Li H. , Chen J. .
Source: Genetic Epidemiology, 2016 11; 40(7), p. 579-590.
PMID: 27550412
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Using family members to augment genetic case-control studies of a life-threatening disease.
Authors: Chen L. , Weinberg C.R. , Chen J. .
Source: Statistics In Medicine, 2016-07-20 00:00:00.0; 35(16), p. 2815-30.
EPub date: 2016-07-20 00:00:00.0.
PMID: 26866629
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Retrospective likelihood-based methods for analyzing case-cohort genetic association studies.
Authors: Shen Y. , Cai T. , Chen Y. , Yang Y. , Chen J. .
Source: Biometrics, 2015 Dec; 71(4), p. 960-8.
PMID: 26177343
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The Use Of The Gail Model, Body Mass Index And Snps To Predict Breast Cancer Among Women With Abnormal (bi-rads 4) Mammograms
Authors: McCarthy A.M. , Keller B. , Kontos D. , Boghossian L. , McGuire E. , Bristol M. , Chen J. , Domchek S. , Armstrong K. .
Source: Breast Cancer Research : Bcr, 2015; 17, p. 1.
PMID: 25567532
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Use Of Risk-reducing Surgeries In A Prospective Cohort Of 1,499 Brca1 And Brca2 Mutation Carriers
Authors: Chai X. , Friebel T.M. , Singer C.F. , Evans D.G. , Lynch H.T. , Isaacs C. , Garber J.E. , Neuhausen S.L. , Matloff E. , Eeles R. , et al. .
Source: Breast Cancer Research And Treatment, 2014 Nov; 148(2), p. 397-406.
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