A Hierarchical Bayesian Model to Predict the Duration of Immunity to Haemophilus Influenzas Type B
Article first published online: 25 MAY 2004
Volume 55, Issue 4, pages 1306–1313, December 1999
How to Cite
Auranen, K., Eichner, M., Käyhty, H., Takala, A. K. and Arjas , E. (1999), A Hierarchical Bayesian Model to Predict the Duration of Immunity to Haemophilus Influenzas Type B. Biometrics, 55: 1306–1313. doi: 10.1111/j.0006-341X.1999.01306.x
- Issue published online: 25 MAY 2004
- Article first published online: 25 MAY 2004
- Received February 1998. Revised November 1998. Accepted December 1998.
- Bayesian estimation;
- Hierarchical growth curve models;
- Latent data;
- Markov chain Monte Carlo simulation;
- Subclinical infection with Haemophilus influenzas type
Summary. A hierarchical Bayesian regression model is fitted to longitudinal data on Haemophilus influenzae type b (Hib) serum antibodies. To estimate the decline rate of the antibody concentration, the model accommodates the possibility of unobserved subclinical infections with Hib bacteria that cause increasing concentrations during the study period. The computations rely on Markov chain Monte Carlo simulation of the joint posterior distribution of the model parameters. The model is used to predict the duration of immunity to subclinical Hib infection and to a serious invasive Hib disease.