The overarching goal of the Biostatistics and Informatics Core is to provide statistical, informatic and computational support for the Program Project to conduct advanced and large-scale statistical analyses to identify and characterize the accuracy of risk factors in predicting and screening lung cancer. The Core faculty and other researchers engage in mission-related research motivated by questions and methodological challenges that arise from these projects. The members of this Core have extensive expertise and interests that unite their activities among Projects. By forming a Biostatistics and Informatics Core that functions across projects, we anticipate a more robust and profound level of support for Program research than could be achieved if biostatisticians were nested within each project. The Core allows us to pool resources (e.g. expertise in statistical genetics, statistical and machine learning) across projects and also draw on the broader resources available in multiple participating institutes, such as the Department of Biostatistics at Harvard TH Chan School of Public Health and the Department of Biomedical Data Science at Dartmouth University. The members of the Core have extensive experience in the development and application of new statistical and machine learning methods for the study of genetic susceptibility to cancer risk prediction, and risk assessment. They have worked together over years on studies relating to lung cancer development. The Core provides an environment for coordinating and planning of research across the program projects and to develop and apply state-of-the-art statistical and machine learning methods that meet the needs of this Program Project. To support the Program projects, we propose the following aims: (1) To ensure that all Projects are grounded in sound biostatistical and informatic principles and use state-of-the-art methods for design and analysis; (2) To provide expert advice related to design and analysis in statistical genetics, bioinformatics, and machine learning and statistical learning for all Projects; (3) To conduct mission-related statistical methods research by developing novel statistical methods to address the quantitative needs of the Program Project; (4) To disseminate the proposed statistical methodological developments via expository articles, case studies, and web-shared software; (5) To provide education and training for researchers and students by working with the Steering Committee and the Program investigators. The Core members will coordinate their work activities and effectively support the Program Project by having monthly conference calls and regular email exchanges.
The goal of the Biostatistics and Informatics Core is to provide support for all Program Projects. This Core will support biostatistical analyses and develop new statistical and computational methods to characterize the impact of genetic markers and biomarkers on lung cancer development, to develop comprehensive risk prediction models using genetic markers and biomarkers for lung cancer, and develop risk assessment models using CT-scan imaging features.
|Barfield, Richard; Shen, Jincheng; Just, Allan C et al. (2017) Testing for the indirect effect under the null for genome-wide mediation analyses. Genet Epidemiol 41:824-833|
|Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380|
|Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770|
|Qian, David C; Molfese, David L; Jin, Jennifer L et al. (2017) Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction. BMC Genomics 18:740|
|McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761|