Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14820
Title: Model based estimates of long-term persistence of induced HPV antibodies; a flexible subject-specific approach
Authors: AREGAY, Mehreteab 
SHKEDY, Ziv 
MOLENBERGHS, Geert 
David, Marie-Pierre
TIBALDI, Fabian 
Issue Date: 2013
Source: Journal of Biopharmaceutical Statistics. 23 (6), p. 1228-1248
Abstract: In infectious diseases, it is important to predict the long-term persistence of vaccine-induced antibodies and to estimate the time points where the individual titers are below the treshold value for protection. This article focuses on HPV-16/18, and uses a so-called fractional-polynomial model to this effect, derived in a data-driven fashion. Initially, model selection was done from among the second- and first-order fractional polynomials on the one hand, and the linear mixed model on the other. According to a functional selection procedure, the first-order fractional polynomial was selected. Apart from the fractional polynomial model, we also fitted a power law model, which is a special case of the fractional polynomial model. Both models were compared using Akaike's Information Criterion. Over the observation period, the fractional polynomials fitted the data better than the power-law model; this, of course, does not imply that it fits better over the long run and hence caution ought to be used when prediction is of interest. Therefore, we point out that the persistence of the anti-HPV responses induced by these vaccines can only be ascertained empirically by long-term follow-up analysis.
Notes: Reprint Address: Molenberghs, G (reprint author) - Univ Hasselt, Ctr Stat, Agoralaan 1, B-3590 Diepenbeek, Belgium. E-mail Addresses:geert.molenberghs@uhasselt.be
Keywords: Akaike's information criterion; fractional polynomial model; functional selection procedure; power-law model
Document URI: http://hdl.handle.net/1942/14820
ISSN: 1054-3406
e-ISSN: 1520-5711
DOI: 10.1080/10543406.2013.834917
ISI #: 000325786600002
Rights: © Taylor & Francis Group, LLC
Category: A1
Type: Journal Contribution
Validations: ecoom 2014
Appears in Collections:Research publications

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