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http://hdl.handle.net/1942/20861
Title: | Fast and highly efficient pseudo-likelihood methodology for large and complex ordinal data | Authors: | IVANOVA, Anna MOLENBERGHS, Geert VERBEKE, Geert |
Issue Date: | 2017 | Source: | Statistical methods in medical research, 26(6), p. 2758-2779. | Abstract: | In longitudinal studies, continuous, binary, categorical, and survival outcomes are often jointly collected, possibly with some observations missing. However, when it comes to modeling responses, the ordinal ones have received less attention in the literature. In a longitudinal or hierarchical context, the univariate proportional odds mixed model (POMM) can be regarded as an instance of the generalized linear mixed model (GLMM). When the response of the joint multivariate model encompass ordinal responses, the complexity further increases. An additional problem of model fitting is the size of the collected data. Pseudo-likelihood based methods for pairwise fitting, for partitioned samples and, as introduced in this paper, pairwise fitting within partitioned samples allow joint modeling of even larger numbers of responses. We show that that pseudo-likelihood methodology allows for highly efficient and fast inferences in high-dimensional large datasets. | Notes: | Corresponding author: Anna Ivanova, I-BioStat, KU Leuven, University of Leuven, Leuven, Belgium. Email: anna.ivanova@lstat.kuleuven.be | Keywords: | generalized linear mixed model; proportional odds mixed model; joint modeling; pseudo-likelihood; pairwise fitting; sample partition; asymptotic relative efficiency; reduced computation time | Document URI: | http://hdl.handle.net/1942/20861 | Link to publication/dataset: | https://lirias.kuleuven.be/bitstream/123456789/515312/3/470.pdf | ISSN: | 0962-2802 | e-ISSN: | 1477-0334 | DOI: | 10.1177/0962280215608213 | ISI #: | 000418307900018 | Rights: | © The Author(s) 2015. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2019 vabb 2017 |
Appears in Collections: | Research publications |
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