Best Practices for Designing Study Quality Metrics

Best Practices for Designing Study Quality Metrics

Study Quality Metrics (SQM) is a clinical study oversight strategy designed to monitor trial data on a regular basis.  The goal of SQM is to ensure that study hypotheses are not compromised and to take corrective action as early as possible to enhance or improve the quality of study data, thereby ensuring that study results are valid and credible.

SQM reports typically monitor aspects of study design such as adherence to inclusion and exclusion criteria, dosing and administration, and visit scheduling. These reports also identify potential inconsistencies in the assessment of safety signals across sites or regions and target key aspects of the primary efficacy endpoint, such as the amount of missing data and consistency of patient follow-up.

When designing SQM for our clients, here are some best practices that we employ:

  • SQM  should be study specific.
    The first step in the process of developing SQM reports is to identify key study success factors. In identifying success factors, consideration should be given to:

    • Measurability of these factors
    • Timely availability of data to ensure close to real-time responses
    • Minimum performance standards for each metric
    • Corrective action required 
  • Formulate the SQM plan after protocol finalization.
    Ideally SQM are designed as part of study start up activities, but they can be implemented in ongoing studies as well.
  • Seek input from various teams.
    An SQM strategy should be developed by a team of stakeholders from various functional areas, including Clinical, Statistics, Programming, and Data Management. Make sure all team members are afforded the opportunity to provide input into the creation and definition of various metrics. As a result, should the need for corrective action arise, emphasis is placed on maintaining overall study quality rather than individual functional area performance.

UBC’s Data Management and Biostatistics team can apply SQM to many clinical trials, but not all. In some studies it can be difficult to identify a feasible metric. For example, studies with very few subjects may not accumulate enough information to act on or there may not be enough time to process and react to metrics in studies that are very short. Contact us to discuss whether SQM are right for your trial, and come back later this week when I’ll present three examples that demonstrate how SQM can drive quality data.

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