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Surrogacy assessment using principal stratification and a Gaussian copula model.

Asc Conlon1, Jmg Taylor1, M R Elliott1,2

  • 11 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

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Summary

This study introduces a novel Bayesian method using Gaussian copula models to validate ordinal surrogates for censored failure time outcomes in clinical trials. The approach enhances causal inference for treatment effects on true endpoints.

Keywords:
Gaussian copulacausal inferencepotential outcomessurrogate endpoint

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Epidemiology

Background:

  • Surrogate outcomes (S) in clinical trials offer early insights into treatment (Z) effects on true endpoints (T).
  • Traditional validation methods face challenges due to S being a post-randomization variable and potential unobserved confounders.
  • Principal surrogacy, by Frangakis and Rubin, addresses these by stratifying on the joint distribution of S under treatment and control.

Purpose of the Study:

  • To develop and validate a statistical framework for assessing surrogacy when the surrogate outcome is ordinal and the true endpoint is a censored failure time.
  • To extend the principal surrogacy concept to handle ordinal surrogate markers and time-to-event outcomes.

Main Methods:

  • Utilized a Gaussian copula model to jointly model potential outcomes of the true endpoint (T) and the surrogate outcome (S).
  • Employed a Bayesian estimation approach with appropriate prior distributions due to model non-identification from data.
  • Applied the proposed method to a colorectal cancer clinical trial dataset.

Main Results:

  • Successfully applied the Bayesian Gaussian copula model to assess surrogacy in a real-world clinical trial.
  • The method provides a robust framework for causal inference in the presence of ordinal surrogates and censored data.
  • Demonstrated the utility of the principal surrogacy framework for validating surrogate endpoints in complex scenarios.

Conclusions:

  • The proposed Bayesian method offers a valuable tool for validating ordinal surrogate outcomes for censored time-to-event endpoints in clinical trials.
  • This approach enhances the reliability of surrogate endpoints for inferring treatment effects on true clinical outcomes.
  • The study contributes to advancing statistical methods for causal inference and surrogate endpoint validation.