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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
1P50CA168505-01A1
Application #
8588552
Study Section
Special Emphasis Panel (ZCA1-RPRB-7 (M1))
Project Start
2013-09-25
Project End
2018-07-31
Budget Start
2013-09-25
Budget End
2014-07-31
Support Year
1
Fiscal Year
2013
Total Cost
$140,841
Indirect Cost
$38,002
Name
Ohio State University
Department
Type
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Liyanarachchi, Sandya; Li, Wei; Yan, Pearlly et al. (2016) Genome-Wide Expression Screening Discloses Long Noncoding RNAs Involved in Thyroid Carcinogenesis. J Clin Endocrinol Metab 101:4005-4013
Hollingsworth, Brynn; Senter, Leigha; Zhang, Xiaoli et al. (2016) Risk Factors of (131)I-Induced Salivary Gland Damage in Thyroid Cancer Patients. J Clin Endocrinol Metab 101:4085-4093
Justiniano, Steven E; McElroy, Joseph P; Yu, Lianbo et al. (2016) Genetic variants in thyroid cancer distant metastases. Endocr Relat Cancer 23:L33-6
Nabhan, Fadi; Ringel, Matthew D (2016) Thyroid nodules and cancer management guidelines: comparisons and controversies. Endocr Relat Cancer :
Shirley, Lawrence A; McCarty, Samantha; Yang, Ming-Chen et al. (2016) Integrin-linked kinase affects signaling pathways and migration in thyroid cancer cells and is a potential therapeutic target. Surgery 159:163-70
Danysh, Brian P; Rieger, Erin Y; Sinha, Deepankar K et al. (2016) Long-term vemurafenib treatment drives inhibitor resistance through a spontaneous KRAS G12D mutation in a BRAF V600E papillary thyroid carcinoma model. Oncotarget 7:30907-23
Nagy, Rebecca; Ringel, Matthew D (2015) Genetic predisposition for nonmedullary thyroid cancer. Horm Cancer 6:13-20
Tomsic, Jerneja; He, Huiling; de la Chapelle, Albert (2015) HABP2 Mutation and Nonmedullary Thyroid Cancer. N Engl J Med 373:2086
He, Huiling; Li, Wei; Liyanarachchi, Sandya et al. (2015) Multiple functional variants in long-range enhancer elements contribute to the risk of SNP rs965513 in thyroid cancer. Proc Natl Acad Sci U S A 112:6128-33
He, Huiling; Li, Wei; Liyanarachchi, Sandya et al. (2015) Genetic predisposition to papillary thyroid carcinoma: involvement of FOXE1, TSHR, and a novel lincRNA gene, PTCSC2. J Clin Endocrinol Metab 100:E164-72

Showing the most recent 10 out of 18 publications