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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 Abrahantas, Jose
Molenberghs, Geert
Renard, Didier
Burzykowski, Tomasz
Buyse, Marc E.
Parmar, Mahesh
Stijnen, Theo
Wolfinger, Russ
Keywords: Clustered data
Clinical trials
Issue Date: 2003
Citation: 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).
URI: http://hdl.handle.net/1942/421
DOI: 10.1080/0094965031000062177
ISI #: 000185003800002
ISSN: 0094-9655
Category: A1
Type: Journal Contribution
Validation: ecoom, 2004
Appears in Collections: Research publications

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