THOUGHT LEADERSHIP

Clinical trials are rapidly evolving and becoming more complex as they continue to adopt novel technologies and approaches. Factors such as the number of sites and countries involved, study phase, indication, design, procedures, and the number of data sources and data points collected contribute to this complexity, imposing a strain on study execution. Furthermore, in recent years, the biopharmaceutical industry has witnessed changes in business practices and an increase in merger and acquisition activity.

Data Management (DM) and Clinical Operational teams must work hand in hand to properly execute the capture, management, and monitoring of clinical data throughout the entire life cycle of a study; however, the use of different systems, processes, and datasets makes close and effective collaboration a challenge.

All clinical data managers (CDMs) are familiar with the difficulties of ensuring that laboratory values are compared to the local laboratory normal ranges that were valid at the time of the measurement. In studies with many local laboratories, and in global studies, the same test may be reported in different units across the study. To help alleviate these problems, some studies use central laboratories. Yet there are some problems with this approach as well. Shipping samples from study sites to the central laboratory incurs extra cost and runs the risk that the sample will be unusable when it arrives.