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

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-02, Vol.10 (3), p.465
Main Authors: Del Carmen Pardo, María, Zhao, Qian, Jin, Hua, Lu, Ying
Format: Article
Language:English
Subjects:
Biomarkers
Cancer
clinical trial design
Clinical trials
Entropy
Entropy (Information theory)
Evaluation
Food science
Havrda and Charvat entropy
Hypothesis testing
Information theory
Multiple sclerosis
mutual information
Random variables
surrogate endpoint
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10030465