CRAB utilizes many tools to collect, evaluate and analyze trial data. Our tools are extensions of the knowledge and experience of our staff and assist them in providing seamless and expert service. The tools are developed by programmers with extensive experience and used by staff with cancer clinical trials experience.
To collect data, our electronic data capture (EDC) system features a web-based user interface with field-level edit checks to ensure accurate data entry. Randomization and registration modules facilitate patient registration into appropriate trials and treatment regimens. A calendar tab enables clinical research coordinators (CRCs) to view, at a glance, the current submission status for a patient’s entire form set. User and site management is securely handled through role-based security with a user administration tool for setting user permissions. User accounts are valid across multiple trials. Study documents are readily available as needed. Lab normal values are included in lab reporting forms to ensure accuracy. Images (e.g., MRIs, PETs, CTs, photomicrographs, and X-rays) and associated data are transported to reviewers and collected in a central repository at CRAB using a secure imaging network.
To evaluate data, data managers perform quality control review and evaluation of eligibility, treatment compliance, disease response and adverse events. A patient evaluation tool allows data managers to enter derived data pertaining to eligibility, treatment, and response with comprehensive cross-field edit checks. An automated query tracking tool submits queries for incorrect data, and institutions resolve the queries automatically by resubmitting the form with corrected data. The query tracking tool also allows data managers to write and manage data queries.
To analyze data, biostatisticians use SAS® as the primary statistical package to analyze data which is primarily stored within an Oracle® database. SAS® is supplemented by associated in-house programs written using SAS, R and SPLUS® to perform data analyses. Common tasks include Cox regression analysis, Kaplan-Meier survival curves, statistical testing, recursive partitioning, and longitudinal data analysis. Applications used in microarray analysis include R and Bioconductor. A high speed computing system (HSCS) for genomics data analysis is used to analyze gene expression profiling (GEP) data.