External Validation
The reliability and acceptability of a prediction model largely depend on how well it performs in a validation cohort, outside of the derivation cohort where the model was developed. Internal validation of prediction models is often not sufficient for generalizability, and external validation is necessary before implementing prediction models in clinical practice. External validation of models is often considered essential to support the general applicability of a prediction model as it addresses transportability1, 2. Transportability requires the model to perform accurately in predicting data drawn from a different but plausibly related population or in data collected by using a little different method than those used in the development sample3. External validation requires data collected from a similar group of patients in a different setting and aims to address the accuracy and performance of a prediction model in a different patient population. These data (sample) are fully independent of the development data and originate from different but similar patients. The generalizability of a model becomes stronger when the model is externally validated multiple times and in a more diverse setting2. This is the reason perhaps why the Framingham Risk Score (FRS) for cardiovascular disease (CVD) is so widely used in the clinical setting as the model was externally validated many times with many different settings.
Most studies evaluating prediction models focus on the issue of internal validity as opposed to the important issue of external validity. Internal validation does not guarantee generalizability, and thus external validation is necessary before implementing prediction models into clinical practice.
External validation can be assessed in different ways:
  1. Temporal validation (validation in more recent individuals)
  2. Geographical validation (validation in other places)
  3. Fully independent validation/Strong external validation (by other investigators at other sites)