White Paper

Local Lab Normalization

Source: LabConnect
Laboratory

Local laboratory data normalization is a routine process in clinical trial data management. However, even with standardized collection and transport procedures across the laboratories there is often variability to account for. For instance, hemoglobin has different reference ranges for males and females, which requires separate lab normalization.1

Without commensurable measurements, making direct comparisons across local laboratory sites is neither prudent nor advisable. Therefore, those conducting trials at local laboratories with varying reference ranges have grown accustomed to using normalization to standardize results against a single reference range. In order to conduct a proper analysis, sponsors and their central laboratories typically wait to normalize data until after a clinical trial has concluded—which means data managers and sponsors eager to see results have to wait to collect all the data from local laboratories.

However, several problems can arise when normalization is reserved for the end of a trial. Errors in laboratory data collection that require additional time to resolve can impact trial timelines and delay submissions to regulatory agencies.2 In many such cases, database lock—a pivotal step in the submission process—is delayed due to receiving data late or poor-quality data that need to be revised.2

The advent of near real-time lab normalization—that is, normalization that occurs throughout the clinical trial—gives sponsors the tools they require to help mitigate these problems. Although it does not replace a thorough normalization process after trial completion, real-time normalization is a supplementary process that can identify and overcome common issues in data collection to avoid an accumulation of errors or inefficiencies upon closing out a clinical trial. Likewise, supplementing with real-time normalization can also enable sponsors and investigators to garner important information earlier with a glimpse into their overall dataset sooner.

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