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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10813

Title: Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes
Authors: Rizopoulos, Dimitris
Verbeke, Geert
Molenberghs, Geert
Issue Date: 2010
Citation: BIOMETRICS, 66(1). p. 20-29
Abstract: The majority of the statistical literature for the joint modeling of longitudinal and time-to-event data has focused on the development of models that aim at capturing specific aspects of the motivating case studies. However, little attention has been given to the development of diagnostic and model-assessment tools. The main difficulty in using standard model diagnostics in joint models is the nonrandom dropout in the longitudinal outcome caused by the occurrence of events. In particular, the reference distribution of statistics, such as the residuals, in missing data settings is not directly available and complex calculations are required to derive it. In this article, we propose a multiple-imputation-based approach for creating multiple versions of the completed data set under the assumed joint model. Residuals and diagnostic plots for the complete data model can then be calculated based on these imputed data sets. Our proposals are exemplified using two real data sets.
Notes: [Rizopoulos, Dimitris] Erasmus MC, Dept Biostat, NL-3000 CA Rotterdam, Netherlands. [Verbeke, Geert; Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Louvain, Belgium. [Verbeke, Geert; Molenberghs, Geert] Univ Hasselt, B-3590 Diepenbeek, Belgium. d.rizopoulos@erasmusmc.nl
URI: http://hdl.handle.net/1942/10813
DOI: 10.1111/j.1541-0420.2009.01273.x
ISI #: 000275727200004
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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