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Title: | Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R: a word of caution | Authors: | ONG, Fenny Wang, Jingzhao VAN DER ELST, Wim VERBEKE, Geert MOLENBERGHS, Geert ALONSO ABAD, Ariel |
Issue Date: | 2022 | Publisher: | TAYLOR & FRANCIS INC | Source: | Journal of Biopharmaceutical Statistics, 32 (5) , p. 705-716 | Abstract: | The meta-analytic approach has become the gold-standard methodology for the evaluation of surrogate endpoints and several implementations are currently available in SAS and R. The methodology is based on hierarchical models that are numerically demanding and, when the amount of data is limited, maximum likelihood algorithms may not converge or may converge to an ill-conditioned maximum such as a boundary solution. This may produce misleading conclusions and have negative implications for the evaluation of new drugs. In the present work, we explore the use of two distinct functions in R (lme and lmer) and the MIXED procedure in SAS to assess the validity of putative surrogate endpoints in the meta-analytic framework, via simulations and the analysis of a real case study. We describe some problems found with the lmer function in R that led to a poorer performance as compared with the lme function and MIXED procedure. | Notes: | Ong, F (corresponding author), Univ Hasselt, I Biostat, B-3590 Diepenbeek, Belgium. fenny.ong@uhasselt.be |
Keywords: | Surrogate markers;lme;lmer;proc mixed;meta-analytic approach | Document URI: | http://hdl.handle.net/1942/36511 | ISSN: | 1054-3406 | e-ISSN: | 1520-5711 | DOI: | 10.1080/10543406.2021.2011903 | ISI #: | 000734834900001 | Rights: | 2021 Taylor & Francis Group, LLC | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R_ a wo.pdf Restricted Access | Early view | 810.94 kB | Adobe PDF | View/Open Request a copy |
autheursversie.pdf | Peer-reviewed author version | 731.21 kB | Adobe PDF | View/Open |
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