This Administrative Core aims to provide leadership and coordination for the Program, and to promote communication and exchange among the 15 participating biostatistical investigators who are engaged in a variety of substantive chronic disease research efforts. The Core also aims to aid in the identification of future direction and emphases for the Program, and to facilitate progress review and monitoring. Coordination, exchange, and critique take place through a simple committee structure, and include monthly methodology development and progress reporting seminars Involving all Program members. Review of progress and plans is also facilitated by interactions with an external Scientific Advisory Committee.

Public Health Relevance

This research Program aims to identify and address methodologic barriers in chronic disease risk assessment, prevention, early detection, prognosis or treatment, and address these through the development of appropriate and practical statistical strategies and techniques. The Administrative Core facilitates exchange and communication among Program participants, to maximize the relevance and value of these research developments.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA053996-34
Application #
8174894
Study Section
Special Emphasis Panel (ZCA1-GRB-S (M1))
Project Start
1997-01-01
Project End
2016-06-30
Budget Start
2011-07-15
Budget End
2012-06-30
Support Year
34
Fiscal Year
2011
Total Cost
$106,584
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Huang, Yijian; Wang, Ching-Yun (2017) Cox regression with dependent error in covariates. Biometrics :
Prentice, Ross L; Huang, Ying; Neuhouser, Marian L et al. (2017) Prentice et al. Respond to ""Improving Estimation of Sodium Intake"". Am J Epidemiol 186:1047-1048
Gorfine, Malka; Berndt, Sonja I; Chang-Claude, Jenny et al. (2017) Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome. PLoS One 12:e0181269
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2017) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics 73:83-93
Dai, James Y; Liang, C Jason; LeBlanc, Michael et al. (2017) Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics :
Zhou, Qian M; Dai, Wei; Zheng, Yingye et al. (2017) Robust Dynamic Risk Prediction with Longitudinal Studies. Stat Theory Relat Fields 1:159-170
Chlebowski, Rowan T; Aragaki, Aaron K; Anderson, Garnet L et al. (2017) Low-Fat Dietary Pattern and Breast Cancer Mortality in the Women's Health Initiative Randomized Controlled Trial. J Clin Oncol 35:2919-2926
Prentice, Ross L; Huang, Ying; Neuhouser, Marian L et al. (2017) Associations of Biomarker-Calibrated Sodium and Potassium Intakes With Cardiovascular Disease Risk Among Postmenopausal Women. Am J Epidemiol 186:1035-1043
Cheng, Yichen; Dai, James Y; Paulson, Thomas G et al. (2017) Quantification of Multiple Tumor Clones Using Gene Array and Sequencing Data. Ann Appl Stat 11:967-991
Zhao, Wei; Chen, Ying Qing; Hsu, Li (2017) On estimation of time-dependent attributable fraction from population-based case-control studies. Biometrics 73:866-875

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