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Rebecca Knowlton1, Layla Parast1

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This study introduces a new framework to evaluate surrogate markers in real-world observational data, addressing confounding and patient heterogeneity. It enables better assessment of surrogate marker validity beyond randomized trials.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Surrogate markers are crucial in clinical trials, but their evaluation in observational research is challenging due to confounding.
  • Existing methods for surrogate marker evaluation often assume randomized treatment, limiting their applicability to real-world data.
  • There's a need for methods to assess surrogate heterogeneity concerning patient characteristics in non-randomized studies.

Purpose of the Study:

  • To propose a novel framework for assessing surrogate heterogeneity in non-randomized data.
  • To accommodate confounders and quantify heterogeneity in surrogate strength with respect to patient characteristics.
  • To identify patient profiles where a surrogate marker reliably replaces the primary outcome.

Main Methods:

  • Developed a framework utilizing meta-learners to analyze observational data.
  • Employed flexible, off-the-shelf machine learning methods to manage confounding.
  • Quantified surrogate heterogeneity by examining surrogate strength across patient characteristics.

Main Results:

  • The proposed framework successfully quantifies surrogate heterogeneity in non-randomized settings.
  • Demonstrated the ability to identify covariate profiles where surrogate markers are valid replacements for primary outcomes.
  • Simulation studies and an application using hemoglobin A1c as a surrogate for fasting plasma glucose validated the approach.

Conclusions:

  • The framework provides a robust method for evaluating surrogate markers in observational research, overcoming limitations of traditional approaches.
  • This approach enhances the utility of surrogate markers in public health and social science research where randomization is impractical.
  • The findings facilitate more accurate interpretation of surrogate marker performance across diverse patient populations.