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Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy



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Pardo Llorente, María del Carmen and Zhao, Qian and Jin, Hua and Lu, Ying (2022) Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy. Mathematics . ISSN 2227-7390 (Submitted)

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Official URL: https://doi.org/10.3390/math10030465


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.

Item Type:Article
Uncontrolled Keywords:Surrogate endpoint; Information theory; Havrda and Charvat entropy; Mutual information; Clinical trial design
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:76901
Deposited On:06 Mar 2023 11:22
Last Modified:06 Mar 2023 11:28

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