External validation of prognostic models: what, why, how, when and where?

Area of Science:

• Clinical Epidemiology
• Biostatistics
• Health Informatics

Background:

• Prognostic models are increasingly developed for clinical event prediction and decision-making.
• External validation of these models by independent researchers is infrequent.
• Reproducibility and generalizability of models depend on external validation.

Purpose of the Study:

• To provide an overview of methodological considerations for external validation studies.
• To explain the importance of external validation for clinical implementation.
• To guide clinicians and researchers in interpreting and applying validation results.

Main Methods:

• Review of methodological considerations in designing and assessing external validation studies.
• Discussion of statistical analyses and interpretation of validation results.
• Guidance on selecting appropriate prediction models and validation populations.

Main Results:

• External validation is essential for determining model reproducibility and generalizability.
• Understanding validation methodologies enhances the clinical utility of prognostic models.
• Clear interpretation of results aids in translating findings to specific patient populations.

Conclusions:

• External validation is a critical step for the clinical implementation of accurate prognostic models.
• This work provides a framework for understanding and conducting external validation studies.
• Clinicians and researchers can improve decision-making by effectively interpreting and applying model validation results.