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Three Categories Influencing Clinical Trial Data Quality


Study Design Factors

Environmental factors typically relate to the protocol’s design and the scope of study visit interventions, such as devices and human methods that measure and generate study data. These study measurement origins can create levels of data variances.
For example, measuring the circumference of a lymph edema patient’s arm with measuring tape may vary from one coordinator to another; one coordinator may measure the arm with slack in the measuring tape, whereas another may compress the tape around the patient’s arm, both of which yield variances in data measurements; this can be risky if you have more than one coordinator taking the same measurements on one patient.  Another scenario can include differing brands of imaging devices which generate varying data formats, noise frequencies and imaging quality that can be interpreted differently by a medical reviewer.
How to mitigate study design risks:  Think about the protocol design and how you can standardize data measurement across the study. If you plan to use a medical device to conduct study measurements, investigate the device’s standardization specifications to uncover potential differences in data quality and frequency. If the measurement technique requires human intervention, consider implementing training programs that delineate appropriate measurement techniques, and limiting the amount of coordinators taking measurements per patient.  Minimizing data variances will likely reduce the required clinical trial sample size.  This ASE Consensus Statement provides an example of standardizing and managing medical device use for measuring outcomes in clinical trials.


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 Moe AlsumidaieBeat Widler, PhDPeter Schiemann, PhD Applied Clinical Trials
In this article, we elaborate on three categories impacting clinical trial data quality.

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