Evaluating individualized treatment effect predictions: A model-based perspective on discrimination and calibration...

Area of Science:

• Biostatistics
• Clinical Epidemiology
• Health Informatics

Background:

• Growing interest in predicting individualized treatment effects (ITE) necessitates robust model evaluation.
• Existing literature primarily focuses on ITE model development, with limited guidance on performance assessment.
• The potential outcomes framework provides a foundation for defining and comparing ITE estimands.

Purpose of the Study:

• To facilitate the validation of prediction models for individualized treatment effects.
• To examine existing measures of discrimination for benefit and propose novel model-based metrics for ITE.
• To compare the performance of proposed validation statistics using simulated and real-world clinical trial data.

Main Methods:

• Defined estimands using the potential outcomes framework for ITE.
• Examined variations of the c-for-benefit statistic and proposed model-based extensions for discrimination and calibration.
• Utilized simulated data and a randomized trial of acute ischemic stroke treatment for evaluation.
• Developed an R software package for implementing proposed validation methods.

Main Results:

• Proposed model-based statistics demonstrated superior performance regarding bias and accuracy compared to existing methods.
• Resampling methods, while adjusting for optimism, exhibited high variance, limiting their accuracy.
• Independent data validation was identified as the optimal approach for assessing ITE model performance.

Conclusions:

• Model-based statistics offer a reliable method for validating individualized treatment effect models.
• Independent data validation is essential for accurate assessment of ITE prediction models.
• The developed R package aids in the practical implementation of these advanced validation techniques.