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Estimation of the Force of Infection from Current Status Data Using Generalized Linear Mixed Models

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Listed:
  • Harriet Namata
  • Ziv Shkedy
  • Christel Faes
  • Marc Aerts
  • Geert Molenberghs
  • Heide Theeten
  • Pierre Van Damme
  • Philippe Beutels

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.

Suggested Citation

  • Harriet Namata & Ziv Shkedy & Christel Faes & Marc Aerts & Geert Molenberghs & Heide Theeten & Pierre Van Damme & Philippe Beutels, 2007. "Estimation of the Force of Infection from Current Status Data Using Generalized Linear Mixed Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(8), pages 923-939.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:923-939
    DOI: 10.1080/02664760701590525
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
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