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This study reviews statistical methods for validating surrogate markers in clinical trials. It focuses on the "proportion of treatment effect explained" framework, offering practical R code for implementation and discussing future research directions.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Surrogate markers are widely used in clinical studies as alternatives to primary outcomes.
  • Validation is crucial for surrogate markers to reliably assess treatment effectiveness.
  • Existing statistical and clinical research has extensively explored surrogate marker evaluation over 35 years.

Purpose of the Study:

  • To describe available statistical frameworks for evaluating surrogate markers.
  • To focus on the practical implementation of the proportion of treatment effect explained (PTE) framework.
  • To provide R code for implementing these procedures.

Main Methods:

  • Review of statistical frameworks for surrogate marker evaluation.
  • Focus on the PTE framework for both uncensored and censored outcomes.
  • Inclusion of parametric and non-parametric estimation methods.
  • Consideration of multiple surrogates, heterogeneity, and prediction perspectives.

Main Results:

  • The tutorial details various statistical approaches for surrogate marker validation.
  • Practical implementation guidance using R code is provided.
  • Discussion includes advanced topics like the surrogate paradox and heterogeneity.

Conclusions:

  • The PTE framework offers a valuable approach for surrogate marker evaluation.
  • Further research is needed, particularly for using surrogate markers to test treatments in future studies.
  • The study enriches the field with new insights and practical tools for surrogate marker analysis.