Pardo Llorente, María del CarmenZhao, QianJin, HuaLu, Ying2023-06-222023-06-222022-01-312227-739010.3390/math10030465https://hdl.handle.net/20.500.14352/73049Surrogate 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.engjournal articlehttps://doi.org/10.3390/math10030465open access519.22Surrogate endpointInformation theoryHavrda and Charvat entropyMutual informationClinical trial designEstadística matemática (Matemáticas)1209 Estadística