Clinical Data Capture & Management: More Isn’t Always Better

 The study case report form (CRF) is arguably the most important clinical study document. While the protocol drives the conduct of the study, the CRF dictates the data to be captured and ultimately the information available for the final analyses. We need to accurately collect data and it needs to be the right data. Decisions regarding data fields to be included on the CRF should be made with an eye to what is required to complete the analyses, establish study treatment safety, and meet protocol objectives. There is often a tendency to collect additional data “just in case”, or to get a peek, or to ensure there are no protocol deviations. More is not better.

How Clinical Data is Captured

Capturing accurate and useful data is vital to any clinical trial. In 2008 and 2011 the Clinical Data Acquisition Standards Harmonization (CDASH) team released guidelines and best practices that inform these decisions. The first recommendation in CDASH best practices is “necessary data only.” A typical example of collecting unnecessary data may be a Phase 1 study in which one captures the time a subject has been seated or supine prior to a vital signs measurement. Data managers are often asked to include this data point in the CRF so that a sponsor can verify that the protocol was followed. This adds one more data point to be entered, verified, reviewed, and programmed but represents a data point that is rarely necessary for the final analyses.

The CDASH standards have been gaining momentum and acceptance in the years since they were released and updated guidance is coming. While data acquisition standards may seem narrowly focused on just one of the first steps in the flow of data for analysis, the CDASH team very deliberately considered the bigger picture and downstream processes. The fields and language favored in the CDASH standards flow directly into the domain and variable conventions described in Clinical Data Interchange Standards Consortium (CDISC) guidance for programming of datasets for submission to regulatory agencies.

Given that the data we capture, and how we capture it, significantly affects each step in the analyses and in achieving study objectives, it only makes sense to be thoughtful and selective in designing CRFs.

Clinical Data Management Methodologies

Thoughtfully selecting the data to be collected has the added advantage of potentially saving effort and cost at each step in the data management flow. Data entry efforts are reduced when there are fewer data points that must be entered. Having fewer data points also reduces the effort required for source document verification which can mean fewer monitoring visits, providing significant cost savings. Similarly, fewer data points reduces effort during data cleaning. Equally important, limiting collection of redundant data leads to fewer discrepancies translating into fewer queries. When the data to be collected is carefully selected and aligns with CDASH standards and best practices there is the added benefit of reducing effort related to programming of CDISC-compliant datasets.

Clinical data analysis is the final stage of the funnel where the most accurate conclusions can be deduced based on pertinent data gathered. If you’d like to learn more about our capabilities for clinical trials, reach out to our VP of Commercial Development, Kevin.Goudreau@DaVita.com.

Nicole Maciolek, PhD

Nicole Maciolek, PhD, earned her PhD in Molecular Genetics from the Medical College of Wisconsin and then transitioned into the drug development industry.  Over the past 9 years she has held roles in medical writing, data management, project management, and clinical project management.  She is currently the Director of Clinical Data Analysis and Reporting and Project Management at DaVita Clinical Research (DCR), overseeing and significantly expanding the data analysis and reporting functions within DCR.