Speaker
Description
Part of the responsibilities of a statistician working on clinical trials is reviewing tables, listings, and figures (TLFs) to ensure accuracy and compliance. However, the review process can be challenging due to the volume of outputs and the dynamic nature of clinical trial data. A common issue arises when outputs are reviewed and checked, but subsequent data updates or programming changes require recreation of these outputs. Manually re-reviewing all outputs is time-consuming, error-prone, and inefficient, particularly when only a small section of an output may have changed. To address this challenge, an automated comparison workflow implemented in R can efficiently detect differences across two versions of the same output. This R-based method uses intermediate datasets, usually created with SAS, which are the final data inputs of the TLFs. However, using intermediate datasets has some disadvantages, namely this approach does not check for possible updates in titles and footnotes.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | automation, clinical trials, TLFs, output review |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
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| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |