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Grant Details

Grant Number: 1R13CA124365-01 Interpret this number
Primary Investigator: Lin, Xihong
Organization: Harvard School Of Public Health
Project Title: Conferences on Emerging Statistical Issues in Biomedical Research
Fiscal Year: 2006


Abstract

DESCRIPTION (provided by applicant): With the rapid advance of biotechnology especially in genomics and growing complexity of biomedical research, quantitative methods have become increasingly important in design and analysis of basic sciences studies, observational studies and clinical trials in cancer, cardiovascular and other chronic diseases. Modern biomedical research, which has become more interdisciplinary in nature, presents a significant number of emerging statistical and computational issues and places an urgent call for the developments of innovative quantitative methods, especially in observational studies, high-dimensional genomic and proteomic studies, disease detection and prevention, environmental epidemiology, nutritional and physical activity epidemiology, and designs and analysis of long-term randomized and observational studies and the role of causal inference. We propose an annual Conference on Emerging Statistical Issues in Biomedical Research, where each conference focuses on an emerging quantitative research area of most recent biomedical research interest and the particular focus of each conference evolves over time. The purpose of the Conference is to provide a timely interdisciplinary platform for biostatisticians, quantitative researchers and scientists in cancer, cardiovascular and other chronic diseases to critique existing quantitative methods, discuss in-depth emerging statistical issues, identify priorities and disseminate results. The Conference will be hosted by two world-leading biostatistics departments. The first conference entitled, "Emerging Statistical Issues in Biomedical Research: Observational Studies," will be launched in 2006 at the University of Washington campus in Seattle, in conjunction with the Joint Statistical Meeting, in order to publicize the Conference Series. It would be co-sponsored by the Departments of Biostatistics of the University of Washington and the Fred Hutchinson Cancer Research Center. The second to the fifth conferences will be held at the Harvard School of Public Health in Boston between 2007 and 2010, with each focusing on an emerging quantitative research area, e.g., the second conference on "Emerging Statistical Issues in Biomedical Research: Bioinformatics and Genomic Epidemiology." It would be cosponsored by the Department of Biostatistics of the HSPH and the Department of Biostatics and Computational Biology of the Dana-Farber Cancer Institute. Serious efforts would be made to engage women and minorities in Conference activities.



Publications

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
Authors: Li Z. , Li X. , Zhou H. , Gaynor S.M. , Selvaraj M.S. , Arapoglou T. , Quick C. , Liu Y. , Chen H. , Sun R. , et al. .
Source: Nature Methods, 2022-10-27 00:00:00.0; , .
EPub date: 2022-10-27 00:00:00.0.
PMID: 36303018
Related Citations

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.
Authors: Li X. , Li Z. , Zhou H. , Gaynor S.M. , Liu Y. , Chen H. , Sun R. , Dey R. , Arnett D.K. , Aslibekyan S. , et al. .
Source: Nature Genetics, 2020-08-24 00:00:00.0; , .
EPub date: 2020-08-24 00:00:00.0.
PMID: 32839606
Related Citations

Challenges and emerging directions in single-cell analysis.
Authors: Yuan G.C. , Cai L. , Elowitz M. , Enver T. , Fan G. , Guo G. , Irizarry R. , Kharchenko P. , Kim J. , Orkin S. , et al. .
Source: Genome Biology, 2017-05-08 00:00:00.0; 18(1), p. 84.
EPub date: 2017-05-08 00:00:00.0.
PMID: 28482897
Related Citations

Recent Progress In Single-cell Cancer Genomics
Authors: Tsoucas D. , Yuan G.C. .
Source: Current Opinion In Genetics & Development, 2017-01-23 00:00:00.0; 42, p. 22-32.
PMID: 28126650
Related Citations

Estimation using penalized quasilikelihood and quasi-pseudo-likelihood in Poisson mixed models.
Authors: Lin X. .
Source: Lifetime Data Analysis, 2007 Dec; 13(4), p. 533-44.
PMID: 18080833
Related Citations



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