Don't be misled: 3 misconceptions about external validation of clinical prediction models

Area of Science:

• Clinical Epidemiology
• Biostatistics
• Health Informatics

Background:

• Clinical prediction models (CPMs) are crucial for patient care and decision-making.
• Widespread implementation of CPMs is hindered by a lack of external validation.
• External validation is often cited as a prerequisite for CPM implementation.

Purpose of the Study:

• To challenge the notion that external validation is always sufficient or necessary for CPM implementation.
• To clarify common misconceptions surrounding the role and interpretation of external validation studies.
• To provide guidance on considering, designing, and interpreting external validation in the context of CPMs.

Main Methods:

• Conceptual analysis and argumentation based on common practices and challenges in clinical prediction model research.
• Discussion of three prevalent misconceptions regarding external validation designs, necessity, and multiplicity.
• Synthesis of insights to inform best practices for model validation and implementation.

Main Results:

• External validation is neither a guaranteed sufficient condition nor an absolute requirement for CPM implementation.
• There is no single recommended design for validation; context is crucial.
• External validation is not always necessary, and multiple external validations may sometimes be required.
• Existing external validation should not be automatically interpreted as a license for implementation.

Conclusions:

• Rethinking the rigid requirement for external validation can facilitate the implementation of valuable CPMs.
• A nuanced understanding of external validation's role is essential for researchers and clinicians.
• The paper aims to improve the thoughtful application and interpretation of external validation in clinical practice.