Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R: a word of caution
Author:
Affiliation:
1. I-Biostat, Universiteit Hasselt, Diepenbeek, Belgium
2. Center for Drug Evaluation, NMPA, People’s Republic of China
3. The Janssen Pharmaceutical, companies of Johnson & Johnson, Beerse, B-2340, Belgium
4. I-BioStat, KU Leuven, Leuven, Belgium
Funder
Special Research Fund (BOF) of Hasselt University (BOF2OCPO3) and by GlaxoSmithKline Biologicals in the context of a civic PhD programme
Publisher
Informa UK Limited
Subject
Pharmacology (medical),Pharmacology,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/10543406.2021.2011903
Reference15 articles.
1. Bates, D., M. Maechler, B. Bolker, S. Walker, R. H. B. Christensen, H. Singmann, B. Dai, F. Scheipl, G. Grothendieck, P. Green, et al. lme4: linear mixed-effects models using ‘eigen’ and S4 (R Package Version 1.1-25). Accessed October 23, 2020. https://cran.r-project.org/web/packages/lme4/index.html
2. The Evaluation of Surrogate Endpoints
3. The validation of surrogate endpoints in meta-analyses of randomized experiments
4. Choice of units of analysis and modeling strategies in multilevel hierarchical models
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