Clinical Prediction Validation, especially external validation, is an essential aspect of developing a predictive model. External validation is necessary to ensure that a prediction model can be applied to patients other than those in the original cohort. To conduct external validation, the model’s output is tested using data that is distinct from the data used to develop the model. Therefore, it is performed after the creation of the prediction model to verify its reliability and effectiveness.

External Validation:

  • External validation encompasses various types of validation, including temporal, geographical, and independent validation. 
  • The issue of sample size calculation estimates based on statistical power considerations has not been extensively explored in external validation studies. 
  • However, a large sample size is necessary to validate a prediction model effectively and produce reliable results in the validation set. 
  • This emphasizes the importance of ensuring that adequate sample size is used in external validation studies to validate the predictive accuracy and generalizability of the model.

Factors that influence and affect external validation data:

  • External validation data can be influenced by various factors, such as the number of events and predictors, which can impact the sample size required for the effective implementation of a prediction model. The sample size required for external validation depends on the number of events and non-events in the sample. 
  • Generally, simulation studies suggest that a minimum of 100 events and/or non-events is needed to validate the prediction model accurately. Small external validation studies are frequently erroneous and ineffective.
  • Clinical prediction models are commonly used in various fields, such as radiology imaging, to predict outcomes based on regression analysis. The choice of predictive model depends on the type of dependent variable. 
  • Linear regression is typically used when the dependent variable is continuous, while logistic regression is used when the dependent variable is binary. When the dependent variable is time-to-event in nature, the Cox-proportional model is used.
  • Several studies have reviewed and validated clinical prediction models for various applications. For instance, Al-Ameri et al. (2020) presented a detailed review of clinical prediction models for liver transplantation, highlighting the importance of external validation. 
  • Ratna et al. (2020) discussed the quality of a clinical prediction model for in vitro fertilization and human reproduction, which was validated using the re-sampling technique and measured using the AUC. 
  • Stevens and Poppe (2020) suggested using the Cox-calibration slope with the logistic regression model instead of only using the predictive model’s calibration slope, after analyzing around 33 research articles and identifying most of the validation as external validation. 
  • Arjun et al. (2020) discussed the development and validation of a clinical prediction model for the mortality study of COVID-19, which underscores the importance of external validation for effective prediction models.

Future Scope:

Although many validation techniques for predictive models have been proposed in the literature, there is no single technique that can be universally applied to all clinical datasets. Moreover, appropriate adjustments to the calibration index are necessary to validate the suitability of prediction models for diverse clinical datasets.

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