A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World...

Area of Science:

• Clinical Trials Methodology
• Real-World Data (RWD) Utilization
• Drug Development Acceleration

Background:

• Novel clinical trial designs are emerging to expedite drug discovery and patient access.
• Routinely collected healthcare data, known as real-world data (RWD), are increasingly used for post-market surveillance.
• Integrating RWD as external comparator arms in clinical trials represents a logical advancement.

Purpose of the Study:

• To propose an evaluation framework for clinical trial designs utilizing RWD external comparator arms.
• To address the potential biases inherent in observational studies using RWD.
• To provide recommendations for the optimal use of RWD external comparators.

Main Methods:

• Defining and applying the principle of exchangeability between external and trial populations.
• Utilizing criteria (e.g., Pocock's criteria) to assess population exchangeability.
• Proposing a four-step process for conducting robust external comparator studies.

Main Results:

• Regulatory bodies are increasingly endorsing external comparators, leading to successful drug approvals.
• The proposed framework facilitates a comprehensive assessment of evidence quality and potential biases.
• The framework integrates covariate balancing, bias analysis, and outcome combination for enhanced reliability.

Conclusions:

• The use of RWD external comparators offers a promising approach to accelerate clinical trials and drug approvals.
• A structured evaluation framework is crucial for maximizing the quality and minimizing bias in these studies.
• Adherence to the proposed four-step process can enhance the validity of evidence derived from RWD external comparator studies.