The proposed Program Project, Statistical Informatics for Cancer Research, will tackle a wide range of challenging statistical problems arising from large, complex datasets in population-based studies in cancer. The Administrative Core will be responsible for providing scientific and administrative leadership for the entire Program.
The specific aims are: (1) To facilitate intellectual exchange and collaboration between all Program members through organizing monthly P01 group meetings, a series of bi-weekly seminars and an annual retreat. Seminars will be open to the broader HSPH community in an effort to stimulate interest in quantitative issues for population based studies in cancer and disseminate findings of the Program Project; (2) To set priorities, and oversee the progress and evaluation of the Program to ensure that appropriate progress is being made and effective communications between the Program investigators, and to work with the External Advisory Committee to monitor and evaluate the progress of the Program Project. (3) To plan short-courses, workshops and visitor programs on topics relevant to the Program mission so as to ensure that all research supported by the Program Project is of high quality and based on cutting edge methods and integrated with substantive cancer areas;(4) To mentor junior members of the Program (postdoctoral fellows and junior faculty);(5) To manage all administrative aspects of the Program, including financial decision making and reporting and annual grant reports;(6) To work with the Statistical Computing Core to ensure appropriate computing support is provided, and effective dissemination of the developed new methodology to real world practices through user-friendly open access software developments, applications of the proposed methods to the motivating cancer data, publications in both statistical and subject-matter conferences, and presentation of results at both statistical and subject-matter conferences. The Core will be co-directed by two accomplished biostatisticians, Professors Xihong Lin and Francesca Dominici, both of whom are also highly experienced and competent administrators.

Public Health Relevance

The Program Project aims to use rich data sources to develop effective strategies for reducing cancer burden in the U.S. and improving longevity and quality of life. The Administrative Gore takes a scientific and administrative leadership of the Program by facilitating exchange and communication among Program participants to maximize the likelihood of accomplishing the mission of the Program.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-RPRB-2)
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Harvard University
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García-Albéniz, Xabier; Maurel, Joan; Hernán, Miguel A (2015) Why post-progression survival and post-relapse survival are not appropriate measures of efficacy in cancer randomized clinical trials. Int J Cancer 136:2444-7
Aschard, Hugues; Vilhjálmsson, Bjarni J; Greliche, Nicolas et al. (2014) Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am J Hum Genet 94:662-76
VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J; Cornelis, Marilyn et al. (2014) Methodological challenges in mendelian randomization. Epidemiology 25:427-35
Krieger, Nancy; Kosheleva, Anna; Waterman, Pamela D et al. (2014) 50-year trends in US socioeconomic inequalities in health: US-born Black and White Americans, 1959-2008. Int J Epidemiol 43:1294-313
Holme, Øyvind; Løberg, Magnus; Kalager, Mette et al. (2014) Effect of flexible sigmoidoscopy screening on colorectal cancer incidence and mortality: a randomized clinical trial. JAMA 312:606-15
Bobb, Jennifer F; Obermeyer, Ziad; Wang, Yun et al. (2014) Cause-specific risk of hospital admission related to extreme heat in older adults. JAMA 312:2659-67
Lee, Seunggeung; Abecasis, Gonçalo R; Boehnke, Michael et al. (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95:5-23
Arvold, Nils D; Wang, Yun; Zigler, Cory et al. (2014) Hospitalization burden and survival among older glioblastoma patients. Neuro Oncol 16:1530-40
Zigler, Corwin Matthew; Dominici, Francesca (2014) Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects. J Am Stat Assoc 109:95-107
Wang, Yun; Schrag, Deborah; Brooks, Gabriel A et al. (2014) National trends in pancreatic cancer outcomes and pattern of care among Medicare beneficiaries, 2000 through 2010. Cancer 120:1050-8

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