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 #
5P01CA053996-35
Application #
8377211
Study Section
Special Emphasis Panel (ZCA1-GRB-S)
Project Start
Project End
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
35
Fiscal Year
2012
Total Cost
$101,397
Indirect Cost
$65,770
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Prentice, Ross L; Zhao, Shanshan (2018) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal 24:3-27
Howard, Barbara V; Aragaki, Aaron K; Tinker, Lesley F et al. (2018) A Low-Fat Dietary Pattern and Diabetes: A Secondary Analysis From the Women's Health Initiative Dietary Modification Trial. Diabetes Care 41:680-687
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
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Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976
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Dai, James Y; Peters, Ulrike; Wang, Xiaoyu et al. (2018) Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 187:2672-2680
Dai, James Y; Liang, C Jason; LeBlanc, Michael et al. (2018) Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics 74:753-763

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