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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-GRB-S)
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Fred Hutchinson Cancer Research Center
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Prentice, Ross L; Zhao, Shanshan (2016) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal :
Dai, James Y; Zhang, Xinyi Cindy; Wang, Ching-Yun et al. (2016) Augmented case-only designs for randomized clinical trials with failure time endpoints. Biometrics 72:30-8
Prentice, R L (2016) Higher Dimensional Clayton-Oakes Models for Multivariate Failure Time Data. Biometrika 103:231-236
Wang, Zhu; Ma, Shuangge; Zappitelli, Michael et al. (2016) Penalized count data regression with application to hospital stay after pediatric cardiac surgery. Stat Methods Med Res 25:2685-2703
Koopmeiners, Joseph S; Feng, Ziding (2016) Group sequential testing of the predictive accuracy of a continuous biomarker with unknown prevalence. Stat Med 35:1267-80
Petralia, Francesca; Song, Won-Min; Tu, Zhidong et al. (2016) New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer. J Proteome Res 15:743-54
Bryan, Matthew; Heagerty, Patrick J (2016) Multivariate analysis of longitudinal rates of change. Stat Med 35:5117-5134
Cheng, Yichen; Dai, James Y; Kooperberg, Charles (2016) Group association test using a hidden Markov model. Biostatistics 17:221-34
Dai, James Y; Tapsoba, Jean de Dieu; Buas, Matthew F et al. (2016) Constrained Score Statistics Identify Genetic Variants Interacting with Multiple Risk Factors in Barrett's Esophagus. Am J Hum Genet 99:352-65
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2016) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics :

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