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Evaluation of Surrogate Endpoints Using Information-Theoretic Measure of Association Based on Havrda and Charvat Entropy

Author

Listed:
  • María del Carmen Pardo

    (Department of Statistics and O.R., Complutense University of Madrid, 28040 Madrid, Spain
    These authors contributed equally to this work.)

  • Qian Zhao

    (Department of Epidemiology and Health Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 510260, China
    These authors contributed equally to this work.)

  • Hua Jin

    (Department of Probability and Statistics, School of Mathematics, South China Normal University, Guangzhou 510631, China)

  • Ying Lu

    (Department of Biomedical Data Science, Stanford University, San Francisco, CA 94305, USA)

Abstract

Surrogate endpoints have been used to assess the efficacy of a treatment and can potentially reduce the duration and/or number of required patients for clinical trials. Using information theory, Alonso et al. (2007) proposed a unified framework based on Shannon entropy, a new definition of surrogacy that departed from the hypothesis testing framework. In this paper, a new family of surrogacy measures under Havrda and Charvat (H-C) entropy is derived which contains Alonso’s definition as a particular case. Furthermore, we extend our approach to a new model based on the information-theoretic measure of association for a longitudinally collected continuous surrogate endpoint for a binary clinical endpoint of a clinical trial using H-C entropy. The new model is illustrated through the analysis of data from a completed clinical trial. It demonstrates advantages of H-C entropy-based surrogacy measures in the evaluation of scheduling longitudinal biomarker visits for a phase 2 randomized controlled clinical trial for treatment of multiple sclerosis.

Suggested Citation

  • María del Carmen Pardo & Qian Zhao & Hua Jin & Ying Lu, 2022. "Evaluation of Surrogate Endpoints Using Information-Theoretic Measure of Association Based on Havrda and Charvat Entropy," Mathematics, MDPI, vol. 10(3), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:465-:d:739236
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