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Related Experiment Videos

Assessing surrogates as trial endpoints using mixed models.

Edward L Korn1, Paul S Albert, Lisa M McShane

  • 1Biometric Research Branch, EPN-8128, National Cancer Institute, Bethesda, MD 20892, USA. korne@ctep.nci.nih.gov

Statistics in Medicine
|October 30, 2004
PubMed
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This study presents methods to evaluate if surrogate endpoints in clinical trials can replace definitive endpoints. It focuses on trial-level surrogacy, considering treatment arm ordering and using mixed models for predictions.

Area of Science:

  • Clinical Trial Methodology
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Definitive endpoints in clinical trials can be impractical, invasive, or time-consuming to obtain.
  • Surrogate endpoints offer a potential alternative when definitive endpoints are challenging to measure.

Purpose of the Study:

  • To discuss and assess methods for evaluating the validity of surrogate endpoints in clinical trials.
  • To determine if surrogate endpoint results can reliably substitute for definitive endpoint results at the trial level.

Main Methods:

  • Examination of trial-level surrogacy, accounting for potential trial-level effects.
  • Distinguishing between naturally ordered treatment arms (e.g., A vs. A+B) versus distinct arms (A vs. B).
  • Comparison of mixed-effects models with fixed-effects models and individual-level data analysis.

Related Experiment Videos

  • Development of estimators to predict definitive endpoint results from surrogate endpoint data using prior trial results.

    • Main Results:

    • Provides methods for assessing trial-level surrogacy, crucial for interpreting clinical trial outcomes.
    • Offers estimators for predicting definitive endpoint results based on surrogate endpoint data and historical trial information.
    • Suggests graphical displays to aid in the assessment of surrogacy.

    Conclusions:

    • Methods discussed enable robust evaluation of surrogate endpoints in clinical trials.
    • The approach allows for prediction of definitive outcomes, enhancing the utility of surrogate endpoints.
    • Highlights the importance of considering trial-level characteristics in surrogacy assessment.