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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/7821

Title: Estimation of the force of infection from current status data using generalized linear mixed models
Authors: Namata, Harriet
Shkedy, Ziv
Faes, Christel
Aerts, Marc
Molenberghs, Geert
Theeten, Heide
Van Damme, Pierre
Beutels, Phillipe
Issue Date: 2007
Citation: JOURNAL OF APPLIED STATISTICS, 34(8). p. 923-939
Abstract: Based on sero-prevalence data of rubella, mumps in the UK and varicella in Belgium, we show how the force of infection, the age-specific rate at which susceptible individuals contract infection, can be estimated using generalized linear mixed models (McCulloch & Searle, 2001). Modelling the dependency of the force of infection on age by penalized splines, which involve fixed and random effects, allows us to use generalized linear mixed models techniques to estimate both the cumulative probability of being infected before a given age and the force of infection. Moreover, these models permit an automatic selection of the smoothing parameter. The smoothness of the estimated force of infection can be influenced by the number of knots and the degree of the penalized spline used. To determine these, a different number of knots and different degrees are used and the results are compared to establish this sensitivity. Simulations with a different number of knots and polynomial spline bases of different degrees suggest - for estimating the force of infection from serological data the use of a quadratic penalized spline based on about 10 knots.
Notes: Univ Hasselt, Ctr Stat, B-3590 Diepenbeek, Belgium. Univ Antwerp, Ctr Evaluat Vaccinat, Antwerp, Belgium.Shkedy, Z, Univ Hasselt, Ctr Stat, Agoralaan Gebouw D, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/7821
DOI: 10.1080/02664760701590525
ISI #: 000251082600004
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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