Targeted validation: validating clinical prediction models in their intended population and setting.

Area of Science:

• Clinical Epidemiology
• Biostatistics
• Health Informatics

Background:

• Clinical prediction models require rigorous validation for reliable deployment.
• External validation studies often use datasets not representative of the model's intended use population.
• This mismatch can lead to overestimated performance and inappropriate clinical use.

Discussion:

• Introducing "targeted validation" to emphasize performance estimation within the specific intended population and setting.
• Targeted validation sharpens focus on model applicability and relevance.
• Distinguishes between internal and external validation needs based on population overlap.

Key Insights:

• Targeted validation prioritizes relevance over convenience in dataset selection.
• It helps prevent misleading conclusions about model performance.
• Reduces unnecessary research waste by avoiding irrelevant validation efforts.

Outlook:

• Promotes more accurate assessment of clinical prediction model utility.
• May reduce the need for external validation when internal validation is robust and populations match.
• Encourages development of models with greater real-world applicability.