RT Journal Article
T1 Evaluation of surrogate endpoints using information-theoretic measure of association based on Havrda and Charvat entropy
A1 Pardo Llorente, María del Carmen
A1 Zhao, Qian
A1 Jin, Hua
A1 Lu, Ying
AB 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.
PB MDPI
SN 2227-7390
YR 2022
FD 2022-01-31
LK https://hdl.handle.net/20.500.14352/73049
UL https://hdl.handle.net/20.500.14352/73049
LA eng
DS Docta Complutense
RD 14 jul 2025