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

María del Carmen Pardo, Qian Zhao, Hua Jin and Ying Lu
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María del Carmen Pardo: Department of Statistics and O.R., Complutense University of Madrid, 28040 Madrid, Spain
Qian Zhao: Department of Epidemiology and Health Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 510260, China
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

Mathematics, 2022, vol. 10, issue 3, 1-18

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.

Keywords: surrogate endpoint; information theory; Havrda and Charvat entropy; mutual information; clinical trial design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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