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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Analysis

Background:

  • Trial-based economic evaluations are crucial for healthcare decision-making.
  • Common challenges include missing data, correlated costs/effects, baseline imbalances, and skewed data.
  • Existing methods for effectiveness studies may not directly apply to joint cost-effect analysis.

Purpose of the Study:

  • To provide step-by-step guidance on statistical methods for trial-based economic evaluations.
  • To address multiple methodological challenges simultaneously.
  • To improve the quality of evidence for healthcare decision-making.

Main Methods:

  • Combining appropriate statistical methods for handling missing data, correlated costs/effects, baseline imbalances, and skewness.
  • Utilizing a ready-to-use R script for practical application.
  • Illustrating methods with a simulated trial-based economic evaluation.

Main Results:

  • The tutorial demonstrates how to integrate statistical techniques to address complex methodological issues in economic evaluations.
  • An R script is provided for practical implementation.
  • The application is illustrated using simulated data.

Conclusions:

  • Appropriate statistical methods are essential to avoid bias in trial-based economic evaluations.
  • This guidance and R script can enhance the reliability of cost-effectiveness analyses.
  • Improved evidence quality supports efficient allocation of healthcare resources.