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Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.

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  • 11 Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China.

Statistical Methods in Medical Research
|January 1, 2015
PubMed
Summary

New methods for augmented vaccine trials assess immune biomarkers as surrogate endpoints. The semiparametric likelihood (SL) method avoids missingness models, offering efficiency and comparable performance to existing approaches.

Keywords:
Counterfactualmissing at randommissing covariatepotential outcomepseudo-likelihoodsemiparametric likelihoodsurrogate endpoint

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Area of Science:

  • Biostatistics
  • Immunology
  • Clinical Trials

Background:

  • Immune biomarkers are increasingly used as surrogate endpoints in vaccine trials.
  • Principal stratification is a framework for analyzing these biomarkers.
  • Augmented vaccine trials are needed to evaluate principal surrogacy.

Purpose of the Study:

  • To review existing methods for analyzing augmented vaccine trials.
  • To propose new methods, including semiparametric likelihood (SL) and pseudo-likelihood (PL).
  • To compare the performance of these methods.

Main Methods:

  • Review of estimated likelihood and pseudo-score (PS) methods.
  • Proposal and analysis of SL and PL methods for augmented vaccine trials.
  • Simulation experiments to evaluate method performance.

Main Results:

  • The SL method does not require a missingness model, beneficial for incomplete data.
  • SL method is asymptotically efficient.
  • SL and PL methods perform similarly to the PS method in simulations, with PL offering computational advantages.

Conclusions:

  • The proposed SL and PL methods provide valuable tools for analyzing augmented vaccine trials.
  • The SL method offers advantages when dealing with missing immune response data.
  • The PL method presents computational benefits for researchers.