The Bioinformatics Core aims to provide statistical expertise and programming support via a transdisciplinary approach during the implementation of projects 1 to 4, as well as during TREC pilot studies.
The specific aims are: 1). To provide statistical and methodological support to the Harvard TREC Center, and develop new methods of answering research questions raised by the Center;2). To interface epidemiology, basic sciences, and clinical medicine, and ensure that sound statistical methods are incorporated into all research and training activities at the TREC Center;3). To manage and maintain large data sets appropriate for use in secondary epidemiologic analyses regarding obesity and energetic issues, and for developing and refining measures of obesity and cancer-related phenotypes;and 4). To ensure timely sharing of data and their submission for centralized data collection at the Coordinating Center. Under the leadership of Dr. Bernard Rosner, Core members will meet monthly to review statistical and measurement issues across the projects. Members will participate in the annual retreat with presentations on state-of-the-art statistical methodology systems and multilevel analysis. Individual statisticians will meet regularly with the research teams they support and with the lead investigators of each developmental project. The Bioinformatics team has worked closely with all Project Leaders in developing the statistical methods and power calculations described in the proposals. The Core will actively participate in transdisciplinary research as an integral part ofthe TREC Center and will develop new methods (or apply existing methods in novel ways across projects), with a particular focus of working with pilot study recipients and new investigators.
|Quante, Mirja; Kaplan, Emily R; Cailler, Michael et al. (2018) Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nat Sci Sleep 10:13-20|
|Song, Mingyang; Zheng, Yan; Qi, Lu et al. (2018) Longitudinal Analysis of Genetic Susceptibility and BMI Throughout Adult Life. Diabetes 67:248-255|
|Chiu, Yu-Han; Bertrand, Kimberly A; Zhang, Shumin et al. (2018) A prospective analysis of circulating saturated and monounsaturated fatty acids and risk of non-Hodgkin lymphoma. Int J Cancer 143:1914-1922|
|Quante, Mirja; Mariani, Sara; Weng, Jia et al. (2018) Zeitgebers and their association with rest-activity patterns. Chronobiol Int :1-11|
|Song, Mingyang; Zheng, Yan; Qi, Lu et al. (2018) Associations between genetic variants associated with body mass index and trajectories of body fatness across the life course: a longitudinal analysis. Int J Epidemiol 47:506-515|
|Sturgeon, Kathleen M; Schweitzer, Aaron; Leonard, John J et al. (2017) Physical activity induced protection against breast cancer risk associated with delayed parity. Physiol Behav 169:52-58|
|Cespedes Feliciano, Elizabeth M; Quante, Mirja; Weng, Jia et al. (2017) Actigraphy-Derived Daily Rest-Activity Patterns and Body Mass Index in Community-Dwelling Adults. Sleep 40:|
|Alessa, Hala B; Chomistek, Andrea K; Hankinson, Susan E et al. (2017) Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers. Med Sci Sports Exerc 49:1817-1825|
|Murray, Kate; Godbole, Suneeta; Natarajan, Loki et al. (2017) The relations between sleep, time of physical activity, and time outdoors among adult women. PLoS One 12:e0182013|
|Huang, Tianyi; Poole, Elizabeth M; Vetter, Celine et al. (2017) Habitual sleep quality and diurnal rhythms of salivary cortisol and dehydroepiandrosterone in postmenopausal women. Psychoneuroendocrinology 84:172-180|
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