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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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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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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Adoption Maturity Model for Risk-based Quality Management (RBQM) in Clinical Trials.

Maria Florez1, Abigail Dirks2, Linda Sullivan2

  • 1Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA. maria.florez@tufts.edu.

Therapeutic Innovation & Regulatory Science
|February 14, 2025
PubMed
Summary
This summary is machine-generated.

Risk-based Quality Management (RBQM) adoption is highest in larger pharmaceutical companies, with Innovators and Early Adopters implementing most components. Further research is needed to understand adoption paths and barriers for broader implementation.

Keywords:
Adoption MaturityClinical TrialsCritical to Quality FactorsInnovation AdoptionProtocol ComplexityProtocol DesignQuality by DesignRisk-based MonitoringRisk-based Quality Management

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Area of Science:

  • Clinical Trial Management
  • Pharmaceutical Development
  • Regulatory Science

Background:

  • International Council for Harmonisation (ICH) guidelines promote Quality by Design (QbD) in clinical trials.
  • Risk-based Quality Management (RBQM) enhances clinical research quality by mitigating risks to safety and efficacy data.
  • A systematic study mapping RBQM adoption maturity stages is needed to guide organizations and accelerate adoption, especially with new ICH E6 R3 guidance.

Purpose of the Study:

  • To develop an adoption maturity model for Risk-based Quality Management (RBQM).
  • To map RBQM adoption levels by company size.
  • To identify characteristics and barriers of different RBQM maturity cohorts.

Main Methods:

  • A global online survey was conducted with 119 pharmaceutical and biotechnology companies.
  • Responses analyzed 32 distinct RBQM practices in ongoing clinical trials (late 2022-early 2023).
  • Analysis focused on developing a maturity model, mapping adoption by company size, and characterizing maturity cohorts.

Main Results:

  • RBQM adoption is concentrated in larger companies (over 25 trials annually), aligning with Innovators, Early Adopters, and Early Majority cohorts.
  • These companies have implemented most of the 32 RBQM components in at least one trial.
  • Highest reported use is in risk documentation and resolution; lowest use is in duplicate patient detection, statistical data monitoring, and critical to quality factor identification.

Conclusions:

  • Larger companies are leading RBQM adoption, integrating most components, particularly in risk management.
  • Maturity levels vary, with opportunities to improve adoption in specific areas like data monitoring and quality factor identification.
  • Understanding adoption paths and barriers is crucial for broader implementation and alignment with evolving regulatory expectations (ICH E6 R3).