Quantitative Data Sciences Core

Biostatistics:

• Research study design
  • Power/sample size
  • Experimental design
  • Statistical Analysis Plans
  • Data collection tools
• Statistical analysis
  • Variety of statistical methods
  • Data visualization
  • Interpretation of results
  • Manuscript writing
• Clinical Trials
  • Design and development
  • eCRF design with PI and QAM
  • Analysis and reporting
• Grant development
• Clinical trials support (IIT)
• Basic science and clinical studies
• Reproducible research pipelines

Bioinformatics:

• Raw data from NUSea or outside data
• NGSCheckMate — sample validation
• RNA-Sea
  • Bulk or single cell
  • Alignment, transcript quantification, differential expression, pathway analysis
  • scRNA-Seq
• ChIP-Sea
• Variant Analysis
• CRISPR screen analysis
• ATAC-Sea
• Standard pipelines and new pipeline development
  • CETO — modular system of sequence analysis tools and pipelines
• Study design and analysis in collaboration with Biostatistics

Clinical Informatics:

• OncoSET
  • Data management: outcomes registry, biobank, raw sequence files
  • Integration with Epic
• Data Capture (NOT1S/REDCap)
  • Development
  • Work with QAM to standardize across trial
• Analytics for LCC and CTO
  • Patient recruitment to trials
  • Financial tracking and dashboard
  • CCSG/CoC reports
  • LCC Membership DB
• NM EDW
  • Clinical research data analysts
  • EHR data exports
• Pathology Core
  • Biobanking database
  • Disburser — sample disbursing tracking
• Custom clinical databases
  • Prostate, Head and Neck, Melanoma, RadOnc Breast, COVID-19 and Cancer

In addition to the co-directors, each area includes faculty and staff who will work with Lurie Cancer Center members in providing expertise as needed. Core members also engage in the following general activities:

• Collaboration with cancer center members across all programs;
• Collaboration with cancer center members on both the Chicago and Evanston campuses
• Discussion about research objectives and hypotheses;
• Writing informatics and statistical sections for grant applications;
• Active collaboration through funded grant activity;
• Active co-authoring of research findings
• Interaction and integration with other cancer center cores as needed;
• Interaction and integration but not overlapping with cores and entities external to the cancer center as needed (NUCATS, IPHAM, CPS, CDSI, BCC, NUGENE, NUSeq);
• Development of new methodology in Biostatistics and Biomedical Analytics 
• Development and review of cancer related protocols;
• Education through individual contact, group seminars, journal clubs and fellow/junior faculty development;