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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Pattern-based Search of Epigenomic Data Using GeNemo
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Just say no to data listings!

Mercidita Navarro1, Nancy Brucken2, Aiming Yang3

  • 1Genentech, South San Francisco, California, USA.

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|January 3, 2023
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Summary

Static data listings in clinical study reports are often excessive. Exploring alternatives, like those enabled by the Study Data Tabulation Model (SDTM), can improve data review and user experience.

Keywords:
clinical study reportsinteractive data listingsstatic data listings

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Area of Science:

  • Clinical data management
  • Regulatory science
  • Pharmaceutical research

Background:

  • Sponsor companies generate extensive static data listings for Clinical Study Reports (CSRs) and regulatory submissions.
  • This practice stems from perceived requirements or a lack of awareness regarding alternative data visualization methods.
  • Participant-level data review is often cumbersome with traditional static listings.

Purpose of the Study:

  • To explore alternatives to voluminous static data listings in Clinical Study Reports (CSRs).
  • To advocate for the adoption of improved data visualization methods in clinical research.
  • To highlight the benefits of standard data structures like the Study Data Tabulation Model (SDTM) for enhanced data review.

Main Methods:

  • Review of existing practices in generating clinical study data listings.
  • Analysis of the PHUSE white paper on "Data Listings in Clinical Study Reports."
  • Discussion of alternative data viewing strategies enabled by standardized data models.

Main Results:

  • Standardized data structures, such as the Study Data Tabulation Model (SDTM), facilitate alternative data viewing methods.
  • These alternatives offer an improved user experience for reviewing clinical study data.
  • Current practices often involve unnecessary creation of static listings due to perceived obligations.

Conclusions:

  • Sponsors should consider alternatives to traditional static data listings.
  • Leveraging standard data structures like SDTM can enhance data accessibility and review efficiency.
  • Adopting alternative data visualization approaches can streamline regulatory submissions and internal data analysis.