The principal objective of the Biostatistics Core will be to provide project investigators a centralized resource for biostatistics expertise. Statistical issues will be addressed at all levels of investigation: from the design of clinical trials and laboratory experiments, to the maintenance of data quality;and from conclusions based on formal hypothesis testing, to important leads discovered by thorough data exploration. In support of this objective, the specific aims of the Core include: 1. Design: collaborate with project investigators in the design of clinical studies and laboratory experiments, formulation of unambiguous hypotheses and hypothesis testing strategies, and development and validation of predictive models. Analysis: provide support with: formal hypothesis tests in clinical and experimental data that ensure strong conclusions;statistical modeling and sensitivity analyses of prospective and retrospective studies;integrated analyses for discovery, training, and validation of predictive models;exploratory analyses that lead to further studies and experiments;and visual displays of data that clarify conclusions and uncover leads. Oversight and Infrastructure: provide oversight for the clinical trials, including design adherence and interim analyses, web based randomization, and data coordination services. Data Quality Assurance: manage data and coordinate services with the Integrated Clinicopathology and Biorepository Core to ensure high quality, security and investigator accessibility for all clinical and experimental data. Methods Research: Investigate new methodologies to directly address difficult data or design problems. Pilot projects: provide design and data analytic support for pilot projects.
The Biostatistics Core will provide critical support for planning and design of experiments and studies, statistical analyses and display of data, and data management and integrity. This support is designed to ensure that studies yield reliable conclusions, resources are efficiently used, and exploratory analyses uncover important leads.
|Bagheri-Yarmand, Rozita; Sinha, Krishna M; Li, Ling et al. (2018) Combinations of Tyrosine Kinase Inhibitor and ERAD Inhibitor Promote Oxidative Stress-Induced Apoptosis through ATF4 and KLF9 in Medullary Thyroid Cancer. Mol Cancer Res :|
|Puli, Oorvashi Roy; Danysh, Brian P; McBeath, Elena et al. (2018) The Transcription Factor ETV5 Mediates BRAFV600E-Induced Proliferation and TWIST1 Expression in Papillary Thyroid Cancer Cells. Neoplasia 20:1121-1134|
|Abrams, Zachary B; Zucker, Mark; Wang, Min et al. (2018) Thirty biologically interpretable clusters of transcription factors distinguish cancer type. BMC Genomics 19:738|
|Shu, Yi; Yin, Hongran; Rajabi, Mehdi et al. (2018) RNA-based micelles: A novel platform for paclitaxel loading and delivery. J Control Release 276:17-29|
|He, Huiling; Li, Wei; Liyanarachchi, Sandya et al. (2018) The Role of NRG1 in the Predisposition to Papillary Thyroid Carcinoma. J Clin Endocrinol Metab 103:1369-1379|
|Chakedis, Jeffery; Shirley, Lawrence A; Terando, Alicia M et al. (2018) Identification of the Thoracic Duct Using Indocyanine Green During Cervical Lymphadenectomy. Ann Surg Oncol 25:3711-3717|
|Xu, Congcong; Haque, Farzin; Jasinski, Daniel L et al. (2018) Favorable biodistribution, specific targeting and conditional endosomal escape of RNA nanoparticles in cancer therapy. Cancer Lett 414:57-70|
|Segkos, Konstantinos; Porter, Kyle; Senter, Leigha et al. (2018) Neck Ultrasound in Patients with Follicular Thyroid Carcinoma. Horm Cancer 9:433-439|
|Wang, Yanqiang; He, Huiling; Liyanarachchi, Sandya et al. (2018) The role of SMAD3 in the genetic predisposition to papillary thyroid carcinoma. Genet Med 20:927-935|
|Valenciaga, Anisley; Saji, Motoyasu; Yu, Lianbo et al. (2018) Transcriptional targeting of oncogene addiction in medullary thyroid cancer. JCI Insight 3:|
Showing the most recent 10 out of 50 publications