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http://hdl.handle.net/1942/30008
Title: | A closed-form estimator for meta-analysis and surrogate markers evaluation | Authors: | FLOREZ POVEDA, Alvaro MOLENBERGHS, Geert VERBEKE, Geert Abad, Ariel Alonso |
Issue Date: | 2019 | Publisher: | TAYLOR & FRANCIS INC | Source: | JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 29(2), p. 318-332 | Abstract: | Estimating complex linear mixed models using an iterative full maximum likelihood estimator can be cumbersome in some cases. With small and unbalanced datasets, convergence problems are common. Also, for large datasets, iterative procedures can be computationally prohibitive. To overcome these computational issues, an unbiased two-stage closed-form estimator for the multivariate linear mixed model is proposed. It is rooted in pseudo-likelihood-based split-sample methodology and useful, for example, when evaluating normally distributed endpoints in a meta-analytic context. However, applications go well beyond this framework. Its statistical and computational performance is assessed via simulation. The method is applied to a study in schizophrenia. | Notes: | [Florez, Alvaro J.; Molenberghs, Geert; Verbeke, Geert] Univ Hasselt, I BioStat, Diepenbeek, Belgium. [Molenberghs, Geert; Verbeke, Geert; Abad, Ariel Alonso] Katholieke Univ Leuven, I BioStat, Leuven, Belgium. | Keywords: | Hierarchical data; linear mixed model; unequal cluster size; surrogacy evaluation; weighting;Hierarchical data; linear mixed model; unequal cluster size; surrogacy evaluation; weighting | Document URI: | http://hdl.handle.net/1942/30008 | ISSN: | 1054-3406 | e-ISSN: | 1520-5711 | DOI: | 10.1080/10543406.2018.1535504 | ISI #: | 000458877800006 | Rights: | 2018 Taylor & Francis Group, LLC | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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10.1080@10543406.2018.1535504.pdf Restricted Access | Published version | 1.49 MB | Adobe PDF | View/Open Request a copy |
JBSpaper.pdf | Peer-reviewed author version | 351.12 kB | Adobe PDF | View/Open |
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