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Joint Modelling of Recurrent Infections and Antibody Response by Bayesian Data Augmentation

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  • Mervi Eerola
  • Dario Gasbarra
  • P. Helena Mäkelä
  • Henri Linden
  • Andrei Andreev

Abstract

. A joint dynamic model for the interdependence between infection, immunity and risk of disease is presented. Recurrent latent infections are modelled as realizations from a renewal process and antibody dynamics as a diffusion with a decreasing drift modified by the stimulating effect of the random infections. The augmented submodels are estimated simultaneously in one large Markov chain Monte Carlo algorithm. As an example, we consider the risk of recurrent ear infections when having only partially observed information on bacterial carriage and antibody concentrations.

Suggested Citation

  • Mervi Eerola & Dario Gasbarra & P. Helena Mäkelä & Henri Linden & Andrei Andreev, 2003. "Joint Modelling of Recurrent Infections and Antibody Response by Bayesian Data Augmentation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(4), pages 677-698, December.
    • Handle: RePEc:bla:scjsta:v:30:y:2003:i:4:p:677-698
    DOI: 10.1111/1467-9469.00358
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    Cited by:

    1. Sheng Luo & Ciprian M. Crainiceanu & Thomas A. Louis & Nilanjan Chatterjee, 2009. "Bayesian Inference for Smoking Cessation with a Latent Cure State," Biometrics, The International Biometric Society, vol. 65(3), pages 970-978, September.
    2. Mansnerus, Erika, 2008. "What happens to facts after their construction?: characteristics and functional roles of facts in the dissemination of knowledge across modelling communities," Economic History Working Papers 22504, London School of Economics and Political Science, Department of Economic History.

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