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Surrogacy assessment using principal stratification with multivariate normal and Gaussian copula models.

Jeremy M G Taylor1, Anna S C Conlon2, Michael R Elliott3

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA jmgt@umich.edu.

Clinical Trials (London, England)
|December 11, 2014
PubMed
Summary

Evaluating surrogate markers (S) for true outcomes (T) in clinical trials can speed up research. This study proposes causal quantities and a Gaussian copula model to assess surrogacy, showing it can distinguish good from poor markers.

Keywords:
Bayesian estimationGaussian copulacausal inferencepotential outcomessurrogate endpoint

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Causal Inference

Background:

  • Validating intermediate markers as surrogates (S) for true endpoints (T) can accelerate clinical trials.
  • Surrogate markers offer a faster and more cost-effective alternative to traditional outcome measures.

Purpose of the Study:

  • Propose causal quantities to evaluate surrogacy within a principal stratification framework.
  • Utilize a Gaussian copula model for an ordinal surrogate and time-to-event outcome.
  • Apply methods to colorectal cancer trial data to assess tumor response as a surrogate for overall survival.

Main Methods:

  • Employ a Bayesian estimation strategy for the Gaussian copula model.
  • Address parameter non-identifiability using informative priors based on reasonable assumptions.
  • Investigate the estimation of surrogacy quantities.

Main Results:

  • The estimation procedure demonstrates reasonable ability to differentiate between poor and good surrogate markers.
  • Some bias was observed in the estimation of surrogacy quantities.
  • The model's performance in distinguishing marker quality was assessed.

Conclusions:

  • The proposed causal quantities, used collectively, can support evidence for a surrogate marker's validity.
  • Model parameter identifiability issues necessitate assumptions for accurate estimation.
  • The study contributes to the methodology for validating surrogate markers in clinical research.