Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/421
Title: | Simplified hierarchical linear models for the evaluation of surrogate endpoints | Authors: | TIBALDI, Fabian CORTINAS ABRAHANTES, Jose MOLENBERGHS, Geert RENARD, Didier BURZYKOWSKI, Tomasz BUYSE, Marc Parmar, Mahesh Stijnen, Theo Wolfinger, Russ |
Issue Date: | 2003 | Publisher: | TAYLOR & FRANCIS LTD | Source: | Journal of Statistical Computation and Simulation, 73(9). p. 643-658 | Abstract: | The linear mixed-effects model (Verbeke and Molenberghs, 2000) has become a standard tool for the analysis of continuous hierarchical data such as, for example, repeated measures or data from meta-analyses. However, in certain situations the model does pose insurmountable computational problems. Precisely this has been the experience of Buyse et al. (2000a) who proposed an estimation- and prediction-based approach for evaluating surrogate endpoints. Their approach requires fitting linear mixed models to data from several clinical trials. In doing so, these authors built on the earlier, single-trial based, work by Prentice (1989), Freedman et al. (1992), and Buyse and Molenberghs (1998). While Buyse et al. (2000a) claim their approach has a number of advantages over the classical single-trial methods, a solution needs to be found for the computational complexity of the corresponding linear mixed model. In this paper, we propose and study a number of possible simplifications. This is done by means of a simulation study and by applying the various strategies to data from three clinical studies: Pharmacological Therapy for Macular Degeneration Study Group (1977), Ovarian Cancer Meta-analysis Project (1991) and Corfu-A Study Group (1995). | Keywords: | linear mixed model; macular degeneration; meta-analytic approach; oncology; random effects; surrogate endpoint | Document URI: | http://hdl.handle.net/1942/421 | ISSN: | 0094-9655 | e-ISSN: | 1563-5163 | DOI: | 10.1080/0094965031000062177 | ISI #: | 000185003800002 | Rights: | (C) 2003 Taylor & Francis Ltd | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2004 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
molg08.pdf | Peer-reviewed author version | 1.85 MB | Adobe PDF | View/Open |
tibaldi2003.pdf Restricted Access | Published version | 129.02 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
29
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
33
checked on Apr 22, 2024
Page view(s)
96
checked on Sep 5, 2022
Download(s)
238
checked on Sep 5, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.