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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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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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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
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Toolkit for ICH E6 (R2) Quality Risk Management for Small to Medium Size Companies.

Andrew Della-Coletta1, Terry Katz2, Kathy Kupka3

  • 1DC Life Sciences Group, Cary, NC, USA.

Therapeutic Innovation & Regulatory Science
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Summary
This summary is machine-generated.

This paper introduces a simple risk management toolkit for clinical studies, designed to help small to medium-sized organizations comply with updated ICH E6 Good Clinical Practice (GCP) guidelines. The toolkit offers customizable templates for essential risk management documents.

Keywords:
ICH E6 (R2)Medium companiesQuality risk managementSmall companiesVendor oversight

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Area of Science:

  • Clinical Research
  • Regulatory Compliance
  • Quality Management Systems

Background:

  • The ICH E6 Good Clinical Practice (GCP) Guideline was significantly revised in 2016, introducing new requirements for systematic risk management and sponsor oversight in clinical studies.
  • Existing risk management solutions are often too complex and costly for small to medium-sized organizations.
  • Compliance with updated GCP guidelines presents challenges for smaller research entities.

Purpose of the Study:

  • To present a simple, robust, and customizable toolkit for clinical study risk management.
  • To facilitate compliance with ICH E6 GCP Guideline revisions for small to medium-sized organizations.
  • To address the complexity and adaptability issues of current risk management solutions.

Main Methods:

  • Development of a toolkit comprising customizable templates.
  • Templates include: Clinical Risk Management SOP, Clinical Risk Management Plan, Vendor Oversight SOP, Vendor Oversight Plan, and Clinical Risk Log.
  • Tools were created by members of the DIA GCP-QA Community.

Main Results:

  • A practical toolkit designed for ease of use by small to medium-sized organizations.
  • The toolkit supports systematic risk management throughout clinical studies.
  • Templates facilitate vendor oversight and risk logging.

Conclusions:

  • The presented toolkit offers a practical solution for small to medium-sized organizations to implement effective clinical study risk management.
  • Adoption of this toolkit can enhance compliance with updated GCP requirements.
  • The toolkit simplifies the implementation of quality management in clinical research for smaller entities.