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Evaluation and comparison of predictive individual-level general surrogates.

Erin E Gabriel1, Michael C Sachs2, M Elizabeth Halloran3

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We introduce a predictive individual-level general surrogate (PIGS) to forecast treatment effectiveness for individuals. This method aids personalized medicine by accurately predicting patient outcomes in new clinical settings.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Personalized Medicine

Background:

  • Intermediate response measures can predict individual treatment efficacy, enabling personalized medical decisions.
  • Evaluating individual-level general surrogates is less developed than trial-level surrogates.
  • Existing methods lack formal cross-validation for quantifying prediction error in new settings.

Purpose of the Study:

  • To define and evaluate a predictive individual-level general surrogate (PIGS).
  • To develop a method for assessing PIGS quality and estimating prediction error in new settings.
  • To compare candidate PIGS using a set of rotavirus vaccine trials.

Main Methods:

  • Utilizing existing individual-level surrogate evaluation techniques within clinical trials.
  • Implementing cross-validation across multiple clinical trials to assess surrogate performance.
  • Estimating the expected absolute prediction error for PIGS in novel trial settings.

Main Results:

  • The proposed method demonstrates robust performance across diverse simulation scenarios.
  • The method successfully evaluates and compares candidate individual-level general surrogates.
  • Application to rotavirus vaccine trials showcases practical utility.

Conclusions:

  • The developed method provides a framework for evaluating individual-level general surrogates.
  • This approach enhances the prediction of individual treatment efficacy in new settings.
  • PIGS can significantly contribute to personalized medicine and clinical decision-making.