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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18866

Title: Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring
Authors: GOEYVAERTS, Nele
Leuridan, E.
FAES, Christel
Van Damme, P.
HENS, Niel
Issue Date: 2015
Citation: STATISTICS IN MEDICINE, 34 (20), p. 2858-2871
Abstract: Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression.We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs.
Notes: Correspondence to: Nele Goeyvaerts, Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Agoralaan 1 Building D, 3590 Diepenbeek, Belgium. nele.goeyvaerts@uhasselt.be
URI: http://hdl.handle.net/1942/18866
DOI: 10.1002/sim.6518
ISI #: 000358421500005
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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