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    Validating clinical trial data

    This paper summarizes the history of clinical data capture through paper and electronic advancements to date and identifies three reasons for the slow movement to more electronic source data.

    The paper then illustrates two methods for the validation of electronic source data.

    This indispensable guide focuses on validating programs written to support the clinical trial process from after the data collection stage to generating reports and submitting data and output to the Food and Drug Administration (FDA).

    Authors Carol Matthews and Brian Shilling provide practical examples, explanations for why different techniques are helpful, and tips for avoiding errors in your output.

    One area of enhanced clinical trial conduct is believed to be available by moving from paper-based source documents to electronic source documents, that is, eliminating paper from clinical data capture, and collecting the information initially in a computer system.

    For both of these reasons, clinical data quality and integrity are critical.

    With the advent of additional electronic capabilities recently with the growth of Internet-based products to enhance business operations in many fields, the clinical trials industry remains uniquely behind most other industries in electronic technology adoptions.

    Valid reasons exist for the slow growth of technology adoptions in clinical trial activities, but there are now discussions about how to use technology more effectively in clinical trial conduct.

    Data validation is a series of documented tests of the data with the goal of ensuring the quality and integrity of the data.

    More specifically, validation is usually concerned with checking four of the eight characteristics of good clinical data – these characteristics are from the first guidance and the first other reference listed below.

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