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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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RWD-Cockpit: Application for Quality Assessment of Real-world Data.

Lmar Marie Babrak1, Erand Smakaj1, Teyfik Agac1

  • 1University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland.

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Summary

A new web application, RWD-Cockpit, offers a framework to evaluate the quality of real-world data (RWD). This tool helps researchers assess RWD quality for regulatory decision-making and evidence generation.

Keywords:
applicationmobile phonequality assessmentreal-world datareal-world evidence

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Area of Science:

  • Health Informatics
  • Data Science
  • Regulatory Science

Background:

  • Digital technologies generate vast amounts of real-world data (RWD) from sources like electronic health records and digital biomarkers.
  • RWD holds potential for drug development, patient stratification, disease correlation discovery, and preventive health measures.
  • A standardized framework for assessing RWD quality is currently lacking, hindering its use in regulatory decision-making.

Purpose of the Study:

  • To develop a web application, RWD-Cockpit, to standardize the evaluation of RWD quality.
  • To translate RWD quality parameters into a quantifiable metric.
  • To propose a framework for preliminary RWD quality assessment.

Main Methods:

  • The RWD-Cockpit systematically scores data sets using proposed quality metrics and user-defined variables.
  • Evaluation is based on seven key variables: manageability, complexity, sample size, privacy, accessibility, periodicity, and standardization.
  • The web application was validated using de novo generated sleep RWD and publicly available data sets.

Main Results:

  • The RWD-Cockpit web application was successfully developed, providing a framework for preliminary RWD quality evaluation.
  • The tool was applied to molecular, phenotypical, and social data sets, identifying variables that can enhance data quality.
  • Case studies using sleep data demonstrated the application's usability in identifying quality-increasing variables.

Conclusions:

  • The RWD-Cockpit framework enables preliminary quality assessment of multiple RWD sets using defined metrics.
  • The generated quality identifiers offer an initial assessment for RWD utilization in regulated settings.
  • This proposal serves as a blueprint for community-driven efforts to establish RWD quality standards.